In the last several years, interest in artificial intelligence (AI) has surged. Venture capital investments in companies developing and commercializing AI-related products and technology have exceeded $2 billion since 2011.1 Technology companies have invested billions more acquiring AI startups. Press coverage of the syllabu has been breathless, fueled by the huge investments and by pundits asserting that computers are starting to kill jobs, will soon be smarter than people, and could threaten the survival of humankind. Consider the following:
Amid all the hype, there is significant commercial activity underway in the area of AI that is affecting or will likely soon affect organizations in every sector. Business leaders should understand what AI really is and where it is heading.
The first steps in demystifying AI are defining the term, outlining its history, and describing some of the core technologies underlying it.
The field of AI suffers from both too few and too many definitions. Nils Nilsson, one of the founding researchers in the field, has written that AI “may lack an agreed-upon definition. . . .”11 A well-respected AI textbook, now in its third edition, offers eight definitions, and declines to prefer one over the other.12 For us, a useful definition of AI is the theory and development of computer systems able to perform tasks that normally require human intelligence. Examples include tasks such as visual perception, speech recognition, decision making under uncertainty, learning, and translation between languages.13Defining AI in terms of the tasks humans do, rather than how humansthink, allows us to discuss its practical applications today, well before science arrives at a definitive understanding of the neurological mechanisms of intelligence.14 It is worth noting that the set of tasks that normally require human intelligence is subject to change as computer systems able to perform those tasks are invented and then widely diffused. Thus, the meaning of “AI” evolves over time, a phenomenon known as the “AI effect,” concisely stated as “AI is whatever hasn’t been done yet.”15
A useful definition of artificial intelligence is the theory and development of computer systems able to perform tasks that normally require human intelligence.
AI is not a new idea. Indeed, the term itself dates from the 1950s. The history of the field is marked by “periods of hype and high expectations alternating with periods of setback and disappointment,” as a accurate apt summation puts it.16 After articulating the bold goal of simulating human intelligence in the 1950s, researchers developed a range of demonstration programs through the 1960s and into the ’70s that showed computers able to accomplish a number of tasks once thought to be solely the domain of human endeavor, such as proving theorems, solving calculus problems, responding to commands by planning and performing physical actions—even impersonating a psychotherapist and composing music. But simplistic algorithms, poor methods for handling uncertainty (a surprisingly ubiquitous fact of life), and limitations on computing power stymied attempts to tackle harder or more diverse problems. Amid disappointment with a lack of continued progress, AI fell out of fashion by the mid-1970s.
In the early 1980s, Japan launched a program to develop an advanced computer architecture that could advance the field of AI. Western anxiety about losing ground to Japan contributed to decisions to invest anew in AI. The 1980s saw the launch of commercial vendors of AI technology products, some of which had initial public offerings, such as Intellicorp, Symbolics,17 and Teknowledge.18 By the end of the 1980s, perhaps half of the Fortune 500 were developing or maintaining “expert systems,” an AI technology that models human expertise with a knowledge base of facts and rules.19 High hopes for the potential of expert systems were eventually tempered as their limitations, including a glaring lack of common sense, the difficulty of capturing experts’ tacit knowledge, and the cost and complexity of building and maintaining large systems, became widely recognized. AI ran out of steam again.
In the 1990s, technical work on AI continued with a lower profile. Techniques such as neural networks and genetic algorithms received fresh attention, in part because they avoided some of the limitations of expert systems and partly because new algorithms made them more effective. The design of neural networks is inspired by the structure of the brain. Genetic algorithms aim to “evolve” solutions to problems by iteratively generating candidate solutions, culling the weakest, and introducing new solution variants by introducing random mutations.
By the late 2000s, a number of factors helped renew progress in AI, particularly in a few key technologies. We explain the factors most responsible for the accurate progress below and then describe those technologies in more detail.
Moore’s Law. The relentless increase in computing power available at a given price and size, sometimes known as Moore’s Law after Intel cofounder Gordon Moore, has benefited all forms of computing, including the types AI researchers use. Advanced system designs that might have worked in principle were in practice off limits just a few years ago because they required computer power that was cost-prohibitive or just didn’t exist. Today, the power necessary to implement these designs is readily available. A dramatic illustration: The current generation of microprocessors delivers 4 million times the performance of the first single-chip microprocessor introduced in 1971.20
Big data. Thanks in part to the Internet, social media, mobile devices, and low-cost sensors, the volume of data in the world is increasing rapidly.21 Growing understanding of the potential value of this data22 has led to the development of new techniques for managing and analyzing very large data sets.23 Big data has been a boon to the development of AI. The reason is that some AI techniques use statistical models for reasoning probabilistically about data such as images, text, or speech. These models can be improved, or “trained,” by exposing them to large sets of data, which are now more readily available than ever.24
The Internet and the cloud. Closely related to the big data phenomenon, the Internet and cloud computing can be credited with advances in AI for two reasons. First, they make available vast amounts of data and information to any Internet-connected computing device. This has helped propel work on AI approaches that require large data sets.25 Second, they have provided a way for humans to collaborate—sometimes explicitly and at other times implicitly—in helping to train AI systems. For example, some researchers have used cloud-based crowdsourcing services like Mechanical Turk to enlist thousands of humans to describe digital images, enabling image classification algorithms to learn from these descriptions.26 Google’s language translation project analyzes feedback and freely offers contributions from its users to Excellerate the quality of automated translation.27
New algorithms. An algorithm is a routine process for solving a program or performing a task. In accurate years, new algorithms have been developed that dramatically Excellerate the performance of machine learning, an important technology in its own right and an enabler of other technologies such as computer vision.28 (These technologies are described below.) The fact that machine learning algorithms are now available on an open-source basis is likely to foster further improvements as developers contribute enhancements to each other’s work.29
We distinguish between the field of AI and the technologies that emanate from the field. The popular press portrays AI as the advent of computers as smart as—or smarter than—humans. The individual technologies, by contrast, are getting better at performing specific tasks that only humans used to be able to do. We call these cognitive technologies (figure 1), and it is these that business and public sector leaders should focus their attention on. Below we describe some of the most important cognitive technologies—those that are seeing wide adoption, making rapid progress, or receiving significant investment.
Computer vision refers to the ability of computers to identify objects, scenes, and activities in images. Computer vision technology uses sequences of imaging-processing operations and other techniques to decompose the task of analyzing images into manageable pieces. There are techniques for detecting the edges and textures of objects in an image, for instance. Classification techniques may be used to determine if the features identified in an image are likely to represent a kind of object already known to the system.30
Computer vision has diverse applications, including analyzing medical imaging to Excellerate prediction, diagnosis, and treatment of diseases;31 face recognition, used by Facebook to automatically identify people in photographs32 and in security and surveillance to spot suspects;33 and in shopping—consumers can now use smartphones to photograph products and be presented with options for purchasing them. 34
Cognitive technologies are products of the field of artificial intelligence. They are able to perform tasks that only humans used to be able to do.
Machine vision, a related discipline, generally refers to vision applications in industrial automation, where computers recognize objects such as manufactured parts in a highly constrained factory environment—rather simpler than the goals of computer vision, which seeks to operate in unconstrained environments. While computer vision is an area of ongoing computer science research, machine vision is a “solved problem”—the subject not of research but of systems engineering.35 Because the range of applications for computer vision is expanding, startup companies working in this area have attracted hundreds of millions of dollars in venture capital investment since 2011.36
Machine learning refers to the ability of computer systems to Excellerate their performance by exposure to data without the need to follow explicitly programmed instructions. At its core, machine learning is the process of automatically discovering patterns in data. Once discovered, the pattern can be used to make predictions. For instance, presented with a database of information about credit card transactions, such as date, time, merchant, merchant location, price, and whether the transaction was legitimate or fraudulent, a machine learning system learns patterns that are predictive of fraud. The more transaction data it processes, the better its predictions are expected to become.
Applications of machine learning are very broad, with the potential to Excellerate performance in nearly any activity that generates large amounts of data. Besides fraud screening, these include sales forecasting, inventory management, oil and gas exploration, and public health. Machine learning techniques often play a role in other cognitive technologies such as computer vision, which can train vision models on a large database of images to Excellerate their ability to recognize classes of objects.37 Machine learning is one of the hottest areas in cognitive technologies today, having attracted around a billion dollars in venture capital investment between 2011 and mid-2014.38 Google is said to have invested some $400 million to acquire DeepMind, a machine learning company, in 2014.39
Natural language processing refers to the ability of computers to work with text the way humans do, for instance, extracting meaning from text or even generating text that is readable, stylistically natural, and grammatically correct. A natural language processing system doesn’t understand text the way humans do, but it can manipulate text in sophisticated ways, such as automatically identifying all of the people and places mentioned in a document; identifying the main syllabu of a document; or extracting and tabulating the terms and conditions in a stack of human-readable contracts. None of these tasks is possible with traditional text processing software that operates on simple text matches and patterns. Consider a single hackneyed example that illustrates one of the challenges of natural language processing. The meaning of each word in the sentence “Time flies like an arrow” seems clear, until you encounter the sentence “Fruit flies like a banana.” Substituting “fruit” for “time” and “banana” for “arrow” changes the meaning of the words “flies” and “like.”40
Natural language processing, like computer vision, comprises multiple techniques that may be used together to achieve its goals. Language models are used to predict the probability distribution of language expressions—the likelihood that a given string of characters or words is a valid part of a language, for instance. Feature selection may be used to identify the elements of a piece of text that may distinguish one kind of text from another—say a spam email versus a legitimate one. Classification, powered by machine learning, would then operate on the extracted features to classify a message as spam or not.41
Because context is so important for understanding why “time flies” and “fruit flies” are so different, practical applications of natural language processing often address relative narrow domains such as analyzing customer feedback about a particular product or service,42automating discovery in civil litigation or government investigations (e-discovery),43 and automating writing of formulaic stories on subjects such as corporate earnings or sports.44
Robotics, by integrating cognitive technologies such as computer vision and automated planning with tiny, high-performance sensors, actuators, and cleverly designed hardware, has given rise to a new generation of robots that can work alongside people and flexibly perform many different tasks in unpredictable environments.45Examples include unmanned aerial vehicles,46 “cobots” that share jobs with humans on the factory floor,47 robotic vacuum cleaners,48and a slew of consumer products, from toys to home helpers.49
Speech recognition focuses on automatically and accurately transcribing human speech. The technology has to contend with some of the same challenges as natural language processing, in addition to the difficulties of coping with diverse accents, background noise, distinguishing between homophones (“buy” and “by” sound the same), and the need to work at the speed of natural speech. Speech recognition systems use some of the same techniques as natural language processing systems, plus others such as acoustic models that describe sounds and their probability of occurring in a given sequence in a given language.50 Applications include medical dictation, hands-free writing, voice control of computer systems, and telephone customer service applications. Domino’s Pizza recently introduced a mobile app that allows customers to use natural speech to order, for instance.51
As noted, the cognitive technologies above are making rapid progress and attracting significant investment. Other cognitive technologies are relatively mature and can still be important components of enterprise software systems. These more mature cognitive technologies include optimization, which automates complex decisions and trade-offs about limited resources;52planning and scheduling, which entails devising a sequence of actions to meet goals and observe constraints;53 and rules-based systems, the technology underlying expert systems, which use databases of knowledge and rules to automate the process of making inferences about information.54
Organizations in every sector of the economy are already using cognitive technologies in diverse business functions.
In banking, automated fraud detection systems use machine learning to identify behavior patterns that could indicate fraudulent payment activity, speech recognition technology to automate customer service telephone interactions, and voice recognition technology to verify the identity of callers.55
In health care, automatic speech recognition for transcribing notes dictated by physicians is used in around half of US hospitals, and its use is growing rapidly.56 Computer vision systems automate the analysis of mammograms and other medical images.57 IBM’s Watson uses natural language processing to read and understand a vast medical literature, hypothesis generation techniques to automate diagnosis, and machine learning to Excellerate its accuracy.58
In life sciences, machine learning systems are being used to predict cause-and-effect relationships from biological data59 and the activities of compounds,60 helping pharmaceutical companies identify promising drugs.61
In media and entertainment, a number of companies are using data analytics and natural language generation technology to automatically draft articles and other narrative material about data-focused subjects such as corporate earnings or sports game summaries.62
Oil and gas producers use machine learning in a wide range of applications, from locating mineral deposits63 to diagnosing mechanical problems with drilling equipment.64
The public sector is adopting cognitive technologies for a variety of purposes including surveillance, compliance and fraud detection, and automation. The state of Georgia, for instance, employs a system combining automated handwriting recognition with crowdsourced human assistance to digitize financial disclosure and campaign contribution forms.65
Retailers use machine learning to automatically discover attractive cross-sell offers and effective promotions.66
Technology companies are using cognitive technologies such as computer vision and machine learning to enhance products or create entirely new product categories, such as the Roomba robotic vacuum cleaner67 or the Nest intelligent thermostat.68
As the examples above show, the potential business benefits of cognitive technologies are much broader than cost savings that may be implied by the term “automation.” They include:
The impact of cognitive technologies on business should grow significantly over the next five years. This is due to two factors. First, the performance of these technologies has improved substantially in accurate years, and we can expect continuing R&D efforts to extend this progress. Second, billions of dollars have been invested tocommercialize these technologies. Many companies are working to tailor and package cognitive technologies for a range of sectors and business functions, making them easier to buy and easier to deploy. While not all of these vendors will thrive, their activities should collectively drive the market forward. Together, improvements in performance and commercialization are expanding the range of applications for cognitive technologies and will likely continue to do so over the next several years (figure 2).
Examples of the strides made by cognitive technologies are easy to find. The accuracy of Google’s voice recognition technology, for instance, improved from 84 percent in 2012 to 98 percent less than two years later, according to one assessment.69 Computer vision has progressed rapidly as well. A standard benchmark used by computer vision researchers has shown a fourfold improvement in image classification accuracy from 2010 to 2014.70 Facebook reported in a peer-reviewed paper that its DeepFace technology can now recognize faces with 97 percent accuracy.71 IBM was able to double the precision of Watson’s answers in the few years leading up to its famous Jeopardy! victory in 2011.72 The company now reports its technology is 2,400 percent “smarter” today than on the day of that triumph.73
Many companies are working to tailor and package cognitive technologies for a range of sectors and business functions, making them easier to buy and easier to deploy.
As performance improves, the applicability of a technology broadens. For instance, when voice recognition systems required painstaking training and could only work well with controlled vocabularies, they found application in specialized areas such as medical dictation but did not gain wide adoption. Today, tens of millions of Web searches are performed by voice every month.74Computer vision systems used to be confined to industrial automation applications but now, as we’ve seen, are used in surveillance, security, and numerous consumer applications. IBM is now seeking to apply Watson to a broad range of domains outside of game-playing, from medical diagnostics to research to financial advice to call center automation.75
Not all cognitive technologies are seeing such rapid improvement. Machine translation has progressed, but at a slower pace. One benchmark found a 13 percent improvement in the accuracy of Arabic to English translations between 2009 and 2012, for instance.76 Even if these technologies are imperfect, they can be good enough to have a big impact on the work organizations do. Professional translators regularly rely on machine translation, for instance, to Excellerate their efficiency, automating routine translation tasks so they can focus on the challenging ones.77
From 2011 through May 2014, over $2 billion dollars in venture capital funds have flowed to companies building products and services based on cognitive technologies.78 During this same period, over 100 companies merged or were acquired, some by technology giants such as Amazon, Apple, IBM, Facebook, and Google.79 All of this investment has nurtured a diverse landscape of companies that are commercializing cognitive technologies.
This is not the place for providing a detailed analysis of the vendor landscape. Rather, we want to illustrate the diversity of offerings, since this is an indicator of dynamism that may help propel and develop the market. The following list of cognitive technology vendor categories, while neither exhaustive nor mutually exclusive, gives a sense of this.
Data management and analytical tools that employ cognitive technologies such as natural language processing and machine learning. These tools use natural language processing technology to help extract insights from unstructured text or machine learning to help analysts uncover insights from large datasets. Examples in this category include Context Relevant, Palantir Technologies, and Skytree.
Cognitive technology components that can be embedded into applications or business processes to add features or Excellerate effectiveness. Wise.io, for instance, offers a set of modules that aim to Excellerate processes such as customer support, marketing, and sales with machine-learning models that predict which customers are most likely to churn or which sales leads are most likely to convert to customers.80 Nuance provides speech recognition technology that developers can use to speech-enable mobile applications.81
Point solutions. A sign of the maturation of some cognitive technologies is that they are increasingly embedded in solutions to specific business problems. These solutions are designed to work better than solutions in their existing categories and require little expertise in cognitive technologies. Popular application areas include advertising,82 marketing and sales automation,83 and forecasting and planning.84
Platforms. Platforms are intended to provide a foundation for building highly customized business solutions. They may offer a suite of capabilities including data management, tools for machine learning, natural language processing, knowledge representation and reasoning, and a framework for integrating these pieces with custom software. Some of the vendors mentioned above can serve as platforms of sorts. IBM is offering Watson as a cloud-based platform.85
If current trends in performance and commercialization continue, we can expect the applications of cognitive technologies to broaden and adoption to grow. The billions of investment dollars that have flowed to hundreds of companies building products based on machine learning, natural language processing, computer vision, or robotics suggests that many new applications are on their way to market. We also see ample opportunity for organizations to take advantage of cognitive technologies to automate business processes and enhance their products and services.86
Cognitive technologies will likely become pervasive in the years ahead. Technological progress and commercialization should expand the impact of cognitive technologies on organizations over the next three to five years and beyond. A growing number of organizations will likely find compelling uses for these technologies; leading organizations may find innovative applications that dramatically Excellerate their performance or create new capabilities, enhancing their competitive position. IT organizations can start today, developing awareness of these technologies, evaluating opportunities to pilot them, and presenting leaders in their organizations with options for creating value with them. Senior business and public sector leaders should reflect on how cognitive technologies will affect their sector and their own organization and how these technologies can foster innovation and Excellerate operating performance.
Read more on cognitive technologies in "Cognitive technologies: The real opportunities for business" on Deloitte University Press.
Deloitte Consulting LLP’s Enterprise Science offering employs data science, cognitive technologies such as machine learning, and advanced algorithms to create high-value solutions for clients. Services include cognitive automation, which uses cognitive technologies such as natural language processing to automate knowledge-intensive processes; cognitive engagement, which applies machine learning and advanced analytics to make customer interactions dramatically more personalized, relevant, and profitable; and cognitive insight, which employs data science and machine learning to detect critical patterns, make high-quality predictions, and support business performance. For more information about the Enterprise Science offering, contact Plamen Petrov (email@example.com) or Rajeev Ronanki (firstname.lastname@example.org).
David Schatsky is a senior manager at Deloitte LLP. He tracks and analyzes emerging technology and business trends, including the growing impact of cognitive technologies, for the firm’s leaders and its clients.
Craig Muraskin is managing director of the innovation group in Deloitte LLP. He works with leadership to set the group’s agenda and overall innovation strategy, and counsels Deloitte’s businesses on their innovation efforts.
Ragu Gurumurthy is national managing principal of the innovation group in Deloitte LLP, guiding overall innovation efforts across all Deloitte’s business units. He advises clients in the technology and telecommunications sectors on a wide range of subjects including innovation, growth, and new business models.
Originally published by Deloitte University Press on dupress.com. Copyright 2015 Deloitte Development LLC.
Senior business leaders and entrepreneurs often struggle to balance the competing interests at play between their personal and professional lives, not to mention private self-care endeavors, often to the detriment of all three. To ensure a meaningful, fulfilling and productive experience in each of these areas—with clients, partners, shareholders, stakeholders, friends, family and loved ones (including oneself)—professionals need to master the art of syncing schedules.
Coaches, in particular, must learn how to sync their schedules with those of others to meet client needs while preserving the time they need to focus on their own priorities. Below, 15 members of Forbes Coaches Council share their best tips for doing just that.
1. Dedicate Times To Working Both In And On Business
Setting yourself up for success starts with creating dedicated times to work in and on your business. For example, I hold each Tuesday, Wednesday and Thursday for client and prospect sessions, while Monday is for me to work on my business: team meetings, planning, finance, marketing, projects and so on. Friday is reserved for projects, scheduled events, travel and such. Structure makes for clarity! - David Taylor-Klaus, DTK Coaching, LLC
2. Have The Courage To Transparently Change Priorities
Set your priorities and always be aware of what influences and determines your sequence of priorities at that moment. Have the courage to change your priorities if you feel you want to and stand up for what serves you best. Being courageous means that as you feel you need to set your priorities, you also verbalize that and make it transparent for all who are important to you. - Judit Ábri von Bartheld, CHN LLC - Coaching Without Borders Hungary (Coaching Határok Nélkül)
3. Sync All Of Your Daily Activities In Your Calendar
The complexity of scheduling time with clients, partners, stakeholders and loved ones is, and will always be, exactly that—complex. However, it isn’t impossible. I’ve found that I’m able to meet others’ needs as well as preserve my own agenda by having my calendar synced. I literally have the times I eat, the times I work out, the times I meditate and my calls all scheduled into my calendar. - Carlos Then, Mr Then Consulting LLC
4. Schedule Your Priorities In Your Diary/To-Do List
Schedule your priorities in your diary/to-do list, as this gives them the same importance as all your other tasks—it elevates their importance in your mind. Also, remember that your priorities don’t have to happen in 30- or 60-minute blocks. You can schedule micro-moments where you tend to that which is important to you in chunks, such as 10 minutes to call a loved one or 15 minutes to check a job website. - Palena Neale, unabridged
Forbes Coaches Council is an invitation-only community for leading business and career coaches. Do I qualify?
5. Say “No” To Low Priorities And Save ‘Shiny Objects’ For Later
Avoid shiny object syndrome. If you cannot avoid shiny objects, save and schedule them. Say “no” to low priorities and stay focused, focused, focused. - Natasha Charles, Intuitive Coaching with Natasha Charles
6. Maintain Free Space In Your Schedule
Always have free space in your schedule for the day, week and month. As Parkinson’s law explains, work has a way of expanding to fill the time allotted for it. The empty space in your schedule allows you to fill it with the pressing demands of others as well as your own that are bound to come up. If not, you can always pull activities from the future up into the present and finish them in advance. - Vinesh Sukumaran, Vinesh Sukumaran Consulting
7. Block Out Personal Time And Other Top Priorities First
Blocking out a schedule is the most effective way to stay focused and productive. Honor your own rhythms and let your people know when you’re typically available for meetings, sessions or work commitments. Make sure you block out personal time and anything else that is a priority first, as this will allow you to be present and energized when engaging with your people. - Nicole Brant-Zawadzki, BZ Coaching
8. Establish And Honor Professional Boundaries
Be clear-eyed about your core values, which should inform your key priorities. Be sure to block out time for those priorities and be committed to making them happen. Sometimes, this means you will need to be willing to say “no” to opportunities that may conflict with those values or priorities. Establish and communicate clear professional boundaries, and honor the boundaries of those you work with. - Jonathan H. Westover, Human Capital Innovations, LLC
9. Focus On Energy Management Versus Time Management
I focus on energy management versus time management. First, understand when you’re most productive doing strategic versus administrative work and most present for your clients or loved ones. Then, block out your calendar knowing when your energy is best suited for an activity. Finally, be sure these activities align with your values. Your ideal clients and loved ones will get the best of you. - Rosie Guagliardo, InnerBrilliance Coaching
10. Keep Your Schedule Responsive To The Season
I try to keep my schedule responsive to the season. At certain times of the year, family time and events take up a lot of my energy. Other times, clients, business projects or exciting new initiatives take a lot of my focus. I try to plan my schedule so my focused energy is best used for the priority of the season. I get better outcomes with flexibility than with rigidity. - Randy Shattuck, The Shattuck Group
11. Take Care Of Yourself Before Others
Put yourself first. You have to nourish yourself and your own business before meeting the demands of others. We want our businesses to grow, and you can only do that if you take care of yourself and your business first. You can still be accommodating and flexible, but with a set of boundaries for yourself as well. - Lauren Najar, Lauren Najar Coaching LLC
12. Organize Daily Items In One, Digital Place
Time is priceless because once spent, you can't get it back. A great tip for syncing your schedule with clients, family, partners and others is to focus on organizing daily items in one place. A digital calendar for personal and professional use helps to maintain structure and balance. Finally, prioritize time in your schedule for personal goals and self-care, which must be treated as nonnegotiables. - Lori A. Manns, Quality Media Consultant Group LLC
13. Sync Schedules With Others Using Google Calendar
One of the best tips for syncing your schedule is to use a tool such as Google Calendar. This can help you see when other people are free and make sure that you’re able to meet their needs while still reserving time for your own priorities. Google Calendar can also be helpful in keeping track of deadlines and other important dates. - Peter Boolkah, The Transition Guy
14. Work Out Your Best Power Routine And Block Time For It
Work out your best power routine and block it out in your calendar. Observe your energy as you go through various activities and determine your acceptable energy level as you progress through the day. Technologically, I synchronize all my client bookings using one calendar, and it gives me the flexibility to add in “rest slots” as the bookings change, ensuring I show up at my best for clients. - Chuen Chuen Yeo, ACESENCE Agile Leadership Coaching and Training Pte. Ltd.
15. Let Technology Help You Control Your Schedule
As a business and life coach, I let technology help me control my schedule. For me, only taking coaching calls on certain days of the week and at certain times really helps me balance my business and personal life. I use a website that only allows openings for coaching calls during certain times on certain days. My clients get to pick what works best for them. - Jessica Stroud, The Lady CEO
After majoring in physics, Kevin Lee began writing professionally in 1989 when, as a software developer, he also created technical articles for the Johnson Space Center. Today this urban Texas cowboy continues to crank out high-quality software as well as non-technical articles covering a multitude of diverse subjects ranging from gaming to current affairs.
Submitted by GoDaddy
By Kayla Schilthuis-Ihrig
Wondering how to take time off? You’re not imagining it — it has, indeed, become increasingly difficult to do. Whatever the cause (and we’ll get to that) the data suggests that the situation is dire.
On the surface, learning how to take time off isn’t mysterious or overly complicated. Yet, action and genuine change remains elusive for many of us. Dive right into these 7 tips for taking more time away from work, or keep studying to learn about the tangible payoffs waiting for you when you do.
Related: 18 self-care tips to promote entrepreneur health and wellness
What kind of time off helps?
There are different manifestations of productive time off. Here are some scenarios that aren’t included on that list:
Rest is not simply the absence of work. It requires a replacement, and the most effective replacement is one that creates contrast. Uncomfortable as it might be in the beginning, time spent working should ultimately be replaced with something that fulfills your other needs.
Deciding to take more time off doesn’t always have to be grandiose.
For this conversation, let’s count any intentional break from work as time off. On the scale of micro-breaks to vacations, there are a whole host of benefits waiting for us.
But before exploring how to take time off and the real rewards that come with stepping away from work, let’s diagnose where work and non-work life balance explosively collide.
Why don’t we take more time off?
Is it our obsession with being busy? Lack of systems in place? Or are we just earnestly procrastinating taking time off?
Maybe we’re not to blame at all. After all, the work-from-home explosion of 2020 led to much longer workdays and destroyed many people’s work-life balance.
But, looking at data dating back to 2000, Forbes points out that “overwork is a longstanding problem.” And that’s never more apparent than when you look at taking vacation time. To get unaffected statistics on the U.S. workforce’s relationship with cashing in vacation days, we have to reach back to a time when the average person had never heard of a coronavirus.
"The U.S. Travel Association reported in 2019 that 55% of American workers left vacation days unspent."
The ocean of statistics on entrepreneurs and their time off is much more shallow, but we don’t need Gallop to tell us that entrepreneurs struggle even more to take time off from work. There’s a chasm of opportunity for most of us to learn how to take time off. Maybe the benefits will sway us.
Related: Mindfulness for entrepreneurs — How to adopt a mindset that can Excellerate your business and your life
Take more time off for your health
The irony here is apparent: The obsession with productivity has such a hold that we must convince ourselves that it’s productive to take more time off.
I mean, we’re already here — might as well lean into it and be blown away by the health benefits, don’t you agree?
We’ve all heard that not taking enough time off from work exasperates stress and the side effects of stress range from headaches to heart disease. Prolonged stress has detrimental effects on our mental and physical health.
That information isn’t new to most of us, but the problem with overworking is that for many people, it’s a constant state of being.
Taking an afternoon off to Excellerate your mental health while still living in a state of overpowering stress is like using a ladle to bail out an overflowing bathtub while the tap is still running.
If it’s meant to offer any lasting health benefits, our ability to take more time off will need to be institutional in our lives, not used as a tool to simply pump the breaks right before collapsing. Instead, successful time off results in something called proactive recovery.
How to take time off: The case for proactive recovery
It’s elegantly simple: choose to fill up the tank before it runs low in the first place. We do this in many other areas of our lives. We all prefer to change the tires of our cars before running them thin and getting a flat. It would be ludicrous to wait for a cavity to brush our teeth.
About our ability to take more time off from work, proactive recovery is the process of catching ourselves right at the point where our performance drops, instead of waiting for a radical loss of productivity.
The big-picture buy-in is easy, but let’s zoom into the macro and find ways to implement taking time off today.
How to take time off in 7 ways
It won’t cost you a cent to initiate each of these actionable tips for taking more time off.
1. Build systems that require you to work less
Systems can be technical, like schedulers and automations. They can also be intangible, like pre-written emails or templates for FAQs, or hiring an assistant or virtual assistant (VA) to handle recurring tasks.
Focus on the three -IONs: automation, delegation and elimination.
A few simple systems worth exploring are:
Editor’s note: If you built your business website using GoDaddy’s Websites + Marketing, you can quickly and easily adapt your website to book appointments, meetings, events, classes and training sessions online.
Finding the right tools is a massive leap towards working smarter and not harder. Milon Mia, CEO of Milsales, described how this process transformed his ability to take more time off:
“I spent 12-18 hours per day working in the beginning for my own business and that took a very big toll on my social, emotional and physical life. I recommend that others not try to figure it all out on their own.”
Instead, he only works about 2-4 hours a day now. “I was able to do this because I built a team, leveraging others’ expertise and resources to propel me forward. The funny thing is now, I can achieve a lot more by working less.”
That’s the dream, isn’t it?
Related: 12 tech shortcuts to relieve work-life stress
2. Book something
A flight, a massage, a walk around the block with a neighbor — big or small, putting time off on the calendar makes it real. It also becomes an event you can prepare for. If your time off spans days or weeks, let your team or clients know in advance.
Present this information as a courtesy notification instead of a request for their blessing.
“If no one objects, I’m going to be out of the office the first week of next month.”
Be firm and confident in your presentation.
“This won’t affect the project deliverables, but I’m letting you know that I’ll be taking time off the first week of next month. I’ve blocked out those days on the calendar, so if you need to schedule a meeting with me, feel free to do so before or after.”
If this time away from work spans multiple days and involves travel, show that you’re not playing around by leaving your laptop at home! You get bonus psychological points if you spend money while scheduling your time off. When we spend money, we’re less likely to cancel or ghost a commitment. It also (should) create an endorphin build-up, which leads right to No. 3.
3. Genuinely look forward to your time off
It’s easy to misconstrue any time off as a success, but it’s not a pass-fail checkbox. There’s a scale. On one end, there’s taking time physically away from your work but still thinking about it. On the other end is something joyful, relaxing, distancing and creativity–inducing.
Who wants to think about their inbox the entire way through an afternoon movie with a friend? That’s not success. Eating yourself nauseous on butter popcorn and having an intense, consuming debate on which Marvel superpowers are most practical for daily life is a success.
But mental multitasking might be step one for you, and that’s OK. There’s likely a lot of unconditioning to do. If your trademark reaction to taking time off work is anxiety about deadlines, productivity or lost progress, it’s really important to reroute your mental reaction. Get the endorphins flowing the moment you commit.
The real payoff of learning how to take time off is bringing more joy into your life.
Then, rinse and repeat.
Related: How to take a vacation when you’re a solopreneur
4. Make it a routine
Guys’ night is the first Saturday of every month. Coworking sessions that morph into wine happy hour every other Friday. A standing hiking date with a friend.
These breaks from work can be big or small, but you should institutionalize them into your calendar and life.
Maybe a mix of both is ideal. A common thread of success is that this opportunity for time off involves other people, which rolls right into tip No. 5.
5. Get other people involved
When I posed the question, “what motivates you to take more time off?” to my online network, the No. 1 response was to get other people invested.
Seeing your kids, friends or partner look forward to doing something together makes you less likely to procrastinate or fall through on your commitment to take more time off.
Another take on this same tip came from Aaron Gray, co-owner and teacher at Practice Makes Perfect Music Studio, who found that building up a reliable core team was fundamental to his ability to take more time off.
“While not everyone can have a co-owner, I do encourage small business owners to appoint a ‘second in command.’ Having this small team of reliable employees not only allows business to progress when I’m not there, but it helps me relax knowing that my business is running smoothly.”
This is only effective if you know what to delegate, so you’ll need to first identify problem areas.
6. Identify problem areas
If learning how to take time off is too big of a task to tackle at once, triage your business and look for problem areas you can pivot into opportunities. Once you identify a problem area, consider which of the three -ION words to apply: automation, delegation or elimination.
Your greatest pain points present your great opportunities.
Laurel Carpenter, the co-founder of Pearl Consulting NYC, shared her insight that led to the elimination of one of the most time-consuming business tasks: social media. “Neither of us are workaholics,” she said of herself and her partner. “We decided to stop doing social media management because it never stops.”
7. Set real(istic) boundaries
At the heart of learning how to take time off are boundaries and follow-through.
As Nadalie Bardo put it, we must “embrace the power of downtime to restore, replenish, revive and rejuvenate the body, mind and spirit.”
Fiber artist Emily McKenzie, the owner of Euca Design, finds that creating achievable boundaries and fully unplugging from work, even the creative side of work, results in more productive “on” time. “The nature of my work is creative and if I don’t nurture that, it shows. Taking breaks pays off in more ways than one.”
The American Psychological Association refers to this as not “exhausting the mental fuel.”
But what can you do if you truly can’t take much or any substantial time off right now?
3 things to do if you can’t take time off *yet*
Are you nodding along to the benefits of taking more time off, but it’s truly not in the cards for your workload right now? Here’s how to absorb some of the benefits anyway.
Combine work with taking time off. A simple manifestation of this is a working vacation, aka a workation where you work remotely from a stimulating and contrasting environment to your normal environment.
Institutionalize microbreaks. My favorite tool for this tactic is the Stretch Clock Google Chrome Extension. Every 25 minutes (or any other interval you set), a guided stretching exercise appears.
If stretching isn’t your style, the Pomodoro Technique is your next best bet. Try a Chrome extension for that as well to keep you from getting sucked into the black hole of your phone.
Turn down the volume on hustle culture. This can manifest in the form of Instagram accounts you follow, podcasts you tune into, or any digital watercooler that you frequent.
Tierra Bonds, the owner of Take Charge Credit Consulting, said that realizing she was stuck in the “boss babe, hustle hard” culture helped her enact change in her life. “I realized I wasn’t going to get a gold medal for working 16-hour days. There were more important ways that I could spend my time like healing, self-care, spending time with family, and just BEING.“
That’s a realization that can benefit us all.
On a scale of one to 10, how important is your mental fuel?
Our busyness is not a badge of honor. We can control the amount of time that work consumes in our day.
According to one study from the U.S. Bureau of Labor Statistics, 79% of private-industry American workers had access to paid vacation time (pre-pandemic). We might not have paid time off the same way as employed individuals, but entrepreneurs can learn from this. It’s time to start taking time off for personal reasons, for self-care, for vacation, and ultimately, for productivity.
Apparently, it’s to our detriment not to.
Or, as my stretching Google Chrome extension put it on one of today’s breaks: “Remember, it doesn’t help to go too far.”
I think this counts for your emotional state as much as it does for your neck muscles.
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More AI is being applied in the real-world and proving its value. Here’s how thought leaders are assessing today’s AI landscape.
When artificial intelligence (AI) technologies first debuted, they didn’t live up to the hype — not even close — and left many early adopters disappointed. But that was then and this is now, and AI is finally catching the attention of more end users, changing the core value proposition of physical and electronic security.
“Many early AI video solutions did disappoint customers with perspective limitations and false alarms,” says Jason Burrows, sales director, IDIS America. “But today, a single camera can provide highly accurate analysis from more angles than ever before and deliver high-performance, even in challenging light conditions.”
He points out that, initially, most AI was offered as software or modules within VMS, which oftentimes entailed expensive integration services, software licenses and maintenance agreements. Now, customers can choose from highly cost-effective AI plug-in appliances that even smaller organizations can benefit from.
“AI video is giving customers additional value in terms of enhancing security and safety, reducing operating costs, and providing actionable intelligence,” he adds. “AI reduces human error and streamlines and automates monotonous tasks, while increasing overall safety and security across single or multiple sites and perimeters.”
Quang Trinh, manager, professional services, Axis Communications, concurs that AI technologies are indeed changing the core value proposition of physical and electronic security.
“Machine learning excels in detecting patterns and it presents great potential in augmenting human tasks that require a lot of repetition,” he says. “As advancements in object classification become more accurate at predicting and labeling objects, the security industry will experience accelerated improvements in reducing false positive alerts. These technologies will produce actionable insight into situations beyond safety and security. AI, predictive analytics, machine and deep learning will continue to expand the value of physical and electronic security solutions by providing operational efficiencies and business intelligence.”
In today’s increasingly connected world, the intelligent video security camera with built-in AI becomes a versatile sensor because it captures data that can deliver valuable information and insights, adds Paul Garms, director of regional marketing – video systems, Bosch. “AI can help users to be proactive, predicting unforeseen or future situations and prevent them from happening. This strengthens the protection of people and property and assists in uncovering business opportunities that create new revenue streams or reduce operational costs.”
Let’s find out what the experts say is realistic today and tomorrow implementing AI for security, safety and operational uses.
Florian Matusek, director of video analytics, Genetec, contends that AI and machine learning are fundamentally changing our industry because they’re making all security, physical security applications data driven.
“AI, on the one hand, creates more data, and analytics is a good example of that where it extracts information out of the video,” he points out. “But on the other hand, it also helps get insights into this data. That’s because the more cameras and the more sensors that are installed, the more data there is. This is where AI helps us to understand this data and create more insights than before.”
Deep neural network-based video analytics is without question improving the accuracy of detection. As Garms points out, this advanced analytics technology relies on deep learning, which uses artificial neural networks that attempt to mimic the human brain, allowing it to learn from large amounts of data and recognize patterns to tackle more complex tasks faster, easier, and more accurately.
This, he says, means it can more accurately detect people and objects, such as vehicles, even in congested scenes.
In addition, many customers today are coming to understand the potential of metadata. It continues to provide more context to events and allows real-time video and recorded footage — large amounts of it — to be quickly organized, searched, retrieved and used.
The functions that are enabled, as a result, Burrows notes, can be broadly categorized into three areas: searching; alarm triggering and notifications; and reporting.
“Previously, without metadata, video footage remained unstructured and difficult to search. Earlier-generation surveillance systems couldn’t ‘watch’ video or interpret it like a human operator could,” he says. “But today’s AI-enabled analytics video solutions can. Today they are performing many core interpretations and sorting functions more quickly, more accurately, and more consistently than operators ever could. To enable this rapid sorting, metadata uses markers such as color, location and time. And, increasingly, it can classify what it sees, defining, for example, all those changing pixels as ‘a person,’ ‘a vehicle,’ or another predefined object of interest; and it can classify by type, size, and movement characteristics such as velocity or location.”
And, as Garms well points out, the power to predict requires making good and efficient use of the rich and versatile data generated by video systems. “The key to this is combining artificial intelligence with the IoT,” he explains. “AIoT products add sense and structure to video data. Their AI capabilities enable them to understand what they’re seeing and to add meaning to captured video with metadata. This is an important first step in converting data into actionable insights and building predictive solutions. This approach is shaping the future of security.”
As Trinh mentions, AI, machine learning and deep learning were already being explored before the pandemic. However, during the pandemic, many organizations began to truly realize the capabilities of their network security system and the role it could play beyond security.
“As organizations sought to keep employees and customers safe and comply with COVID guidelines, many realized the versatility of their network security system and analytics — like using facial recognition to verify mask compliance, occupancy analytics to maintain social distance requirements and access control systems to enable contact tracing.”
Garms echoes that the pandemic increased interest around solutions such as occupancy monitoring and crowd detection, and AI enables more accurate detection and counting of people in an area. While initially investigated for social distancing purposes and for meeting health regulations specific to the pandemic, these solutions also offer benefits beyond security, such as for understanding peak traffic days and times to ensure adequate staffing, he believes, emphasizing that customers will continue to benefit from these solutions.
COVID sped up digitization and automation, particularly across sales and customer service departments to streamline workflows and eliminate mundane tasks, Burrows adds, stating that we’re now seeing that same approach applied to security and facilities management.
“Initially, companies quickly moved to touchless access control readers, but soon realized the greater benefits of frictionless access and visitor management, particularly to support return-to-work strategies and hybrid and flexible working. And we’re now seeing AI video playing its part by enabling frictionless physical and logical access using facial recognition and authentication, for example. And once organizations realize improvements in security, people flow and staff engagement, they look towards the same approach to deliveries using a combination of LPR and video intercoms, which can be extended to logistics operators to streamline the throughput of trucks and goods.”
As Burrows pointed out, AI video is giving end users added value in terms of enhanced security and safety, reduced operating costs and actionable intelligence to help Excellerate efficiencies.
Garms forecasts that while AI capabilities are just beginning to make an impact, he expects to see wider adoption as the industry learns more about the possibilities. Some end users may still be hesitant about AI technology due to exaggerated claims in the past, so it is important for integrators to educate their customers about the latest advancements and improvements in accuracy.
For example, it can differentiate between genuine events and false triggers from challenging environmental conditions or among people and wildlife in perimeter protection applications.
It’s important to take a holistic approach, Burrows advises. End users don’t need to leverage AI or upgrade cameras across their entire building, campus or estate, so adopting AI shouldn’t mean a complete rip and replace.
Another benefit Burrows points to is the fact that rules such as virtual lines, tripwires or people counting can be set up to help understand the interactions that objects and people have within the environment. Normal or abnormal events can be recognized against a defined set of rules, with only the changes in a scene being registered, so that security and safety personnel are alerted only when they need to be.
“Automating these surveillance functions for specific areas with highly accurate alerts can greatly reduce the need for personnel to monitor multiple live camera feeds, effectively reducing operating costs,” he adds.
Advancements in AI-based video analytics have made setup and configuration of these systems easier, Garms notes. For example, deep neural network-based detectors have made it easier to achieve highly accurate detection of people and vehicles.
With these capabilities, solutions can be created that extend beyond simple recording of video to solve security and operational challenges.
According to Trinh, another challenge is interoperability, so integrators should work with a vendor that has compatibility with other systems.
“AI technologies can be very complex and they require a new set of skills to understand and sell its uses,” Trinh says. “This can be very challenging, so it’s important to leverage vendors who have training programs that teach about AI technologies in a holistic way, versus learning only proprietary solutions that don’t apply to others. Remember that AI is an open architecture that has the same principles regardless of the platform you are using.”
Florian cautions that AI is still in the process of maturing right now.
“In the past, AI or Big Data was promising a Ferrari and delivering a bicycle,” Florian claims. “We have a long way to go still, and we need to be very sure, very clear about what it can do and what it cannot do. We’ve come a long way, but there’s still a long way to go yet.”
During the next few years, object classification will Excellerate exponentially, Trinh predicts. He foresees that edge devices will become even more powerful from a processing standpoint and offset costs and resources.
“Users will still need powerful PCs/servers on premise for other types of workloads,” he says. “Currently machine learning is costly and time consuming, but that will improve. Cloud computing for AI will also reduce latency challenges by leveraging edge devices and their processing power. Many will adopt a hybrid approach mixing a combination of Cloud, on-premise and edge devices.
“Images and videos will continue to drive deep learning techniques, and the quality of data will become increasingly as important as the quantity data. There is always a need for more data, but quality data in images and videos will impact AI models and help accelerate the effectiveness of the output or prediction. With better quality data, we’ll see a consolidation of effective AI technology solutions in the market.”
This article originally appeared in CS sister publication Security Sales & Integration and has been edited. Author Erin Harrington has 20+ years’ security industry media experience. Contact her at email@example.com.
Boom lifts are an important component of the proper deployment and completion of many construction projects. However, given the size, scale, and structure of this type of equipment, they can also pose substantial risk and have the potential to cause serious injury if effective safety measures are not followed. These safety tips are important to consider and implement when operating boom lifts and can help prevent injuries, accidents, damage and liability concerns.
Always make sure that the base and the entire circumference of the boom lift are clear of any personnel while the boom lift is in use. The circumference of the lift is often significant and tools can easily fall from the platform and seriously hurt anyone who may be standing below. Keeping the entire area clear minimizes the risk of anyone being hit below by objects that may fall.
Though very rare, it is also possible for boom lifts to tip over. Keeping the area under and around the boom lift clear will help ensure that no one is hurt if the entire structure tips over.
Hiring employees who are properly trained to operate boom lifts and who maintain relevant safety certifications and knowledge, can help keep yourself and your other employees safe while the boom lift is in use. Testing potential new hires with hands-on assignments that demonstrate their operational knowledge of the lift can help detect potential problems and address them before any accidents happen on the genuine job.
A simple but vital safety measure is wearing a harness and ensuring the lanyard is fully secured to the bucket. While it may seem unlikely that an operator will fall out of the platform, even the slightest bump from another piece of equipment or object can throw an operator off-balance and put them at risk of falling. Even a strong gust of wind could knock someone down or off of the platform and cause serious injury.
Each boom lift has a specific weight capacity. It is important to identify and adhere to these restrictions. Going over this limit, even slightly, could potentially make the lift top-heavy and cause it to tip over. One should always account for the weight of the operator on the lift as well as all tools and materials on the platform to ensure that the combined weight isn’t more than the recommended capacity before operating the lift. It’s also important to remember not to use the boom lift for lifting heavy supplies.
When on the platform of a boom lift, it’s easy to be inclined to climb on the edge of the platform to reach something instead of moving the entire boom lift to reach it. However, this is more dangerous than many realize. Climbing or sitting on the edge of the platform significantly increases one’s chances of falling off the platform and can result in serious injury. If there is something that isn’t accessible, communicate with the boom lift operator to help get to a safe position in the platform where you can easily reach what is needed.
When a boom lift is extended very high, the wind can pose a substantial safety concern. If the wind is strong enough, it can knock a boom lift over completely. One should avoid using boom lifts in extremely windy conditions or in other adverse weather to avoid the risk of a boom lift potentially falling over. Each lift has a limit of how much wind it can withstand; reviewing the user manual will help an operator understand the specific restrictions of the lift they are operating and identify strategies for using the lift in challenging weather.
The height at which boom lifts can extend makes this equipment particularly vulnerable to tipping over. Ensuring the boom lift is being operated on even ground and on a stable base will help keep the operator and surrounding workers safe. Using the boom lift brakes correctly is one method that can be implemented to help stabilize the boom lift base.
Properly reviewing the manufacturer’s manual will help an operator understand how to effectively maneuver the boom lift and when it is and isn’t safe to move the lift while it is extended. Although, as a best practice, it’s often best to avoid moving the lift in this manner. Moving a lift while extended creates opportunity for injury and should only be done when it is necessary and only if the manufacturer’s manual specifically condones it. Otherwise, one should lower the boom lift completely, move it and then extend where needed.
Operating boom lifts can pose challenges and risks, but following these safety tips will help prevent accidents and injury on-site.
Jim Arabia is a marketing and branding executive with over 20 years of experience leading business with growth initiatives. In his current role as Vice President of Marketing at BigRentz, the nation's largest rental network for construction equipment, Arabia leads market positioning strategies and creates programs to support the company's strategic vision. His previous experience includes award-winning brand marketing services for technology companies, real estate firms, and professional service firms. Arabia holds a B.A. from California State University, Fullerton and an M.B.A. from Pepperdine University.
Tuesday, July 12, 2022
Smart Manufacturing, often referred to as “Industry 4.0,” refers to the fusion of digital manufacturing techniques with traditional manufacturing techniques. While there are many technologies that can be identified as playing a part in smart manufacturing, this article will focus on four that are currently receiving attention: cloud adoption, the Internet of Things (IoT), machine learning and artificial intelligence, and additive manufacturing. Successful deployment of smart manufacturing technologies can lead to faster, more efficient production that is also safer for factory floor workers. Implementation of these technologies also poses intellectual property challenges to which manufacturers may not be accustomed but that, if managed appropriately, promise great rewards.
Cloud computing refers to the distribution of data and applications over multiple locations, allowing on-demand access to the data and applications from several locations by users. As with many other industries, manufacturers are adopting cloud-based computing techniques to enable agile manufacturing and provide real-time data to the production floor. For example, capacity loading information from several production machines, perhaps located at several different geographic locations, can be shared to a cloud so that it is accessible by a distribution unit in real time. This enables the distribution of work to production machines in an efficient manner.
Market Research Future forecasts $111.9 billion of cloud computing investment in the manufacturing sector. Manufacturers contemplating moving their production processes to the cloud should take a moment to assess whether the new process is patentable. While it may seem counterintuitive that moving an existing manufacturing process to a cloud-based platform would yield patentable subject matter, a brief survey of issued patents shows that changes necessary to modify a process so that it executes properly on a cloud-based platform can, indeed, lead to patentable subject matter. Moreover, newly-generated software routines to implement the cloud-based process are likely the subject of copyright, and protection for such materials should be evaluated.
A related issue for manufacturers moving to cloud-based platforms is the security of their systems and data. Cloud-based systems, because of their inherent interconnectedness with other systems, are susceptible to attack. In 2020, targeted ransomware emerged as a pervasive cyber threat to manufacturing. Such attacks are expected to increase as manufacturing companies adopt increasingly digital profiles. Companies adapting smart manufacturing technology need to protect their intellectual property and the resultant data that is generated. Data breach remediation is also likely to be important; information-stealing attacks make up about a third of cyberattacks on manufacturing concerns, with one in five companies successfully compromised.
The Internet of Things (IoT) refers to inclusion of sensors, processing ability, and communication technology in physical devices. IoT has already begun to change how we view devices in our homes; smart TVs, smart thermostats, and smart appliances are seemingly ubiquitous. That perspective change is coming to manufacturing as well, as several companies race to release a universal operating system for all IoT devices. Beyond the obvious changes to the manufacturing floor itself, manufacturers should be aware of two foundational changes IoT will make to their business: IoT will make protection of trade secrets increasingly difficult, and IoT will radically change the relationship a manufacturer has with the end consumer.
Traditionally, many aspects of a manufacturing line were protected as trade secrets. For example, the exact setting used for a machine to process raw material into the desired result might be something known only to the individuals tasked with running that machine. In the IoT world, that machine is interconnected with other machines, and that interconnectedness makes it a potential target for attack. Successfully compromised machines may provide up their settings, preferences, and other secrets that make a manufacturing line “special.” So again, cybersecurity and data management will need to be priorities, not afterthoughts, in the factory of the future.
Looking outwardly, IoT radically changes the traditional relationship a manufacturer has with the end consumer, as it allows the manufacturer to have access to data regarding use of its end products. While collection of genuine data on consumer usage is a fantastic benefit for manufacturers, it comes with obligations surrounding both the collection of that data and securing the data after it has been collected. Provided that the data collected from end users is done in a transparent, privacy-responsible manner, that data represents a commercial asset that may ultimately prove more valuable than the original business.
The terms “machine learning” and “AI” are usually used to refer to techniques to enable machines to think like human beings. Applications of these techniques in manufacturing can include predictive maintenance, predictive quality and yield, digital twinning, generative design, energy consumption forecasting, and supply chain management. This area of technology may represent the largest opportunity for manufacturers to develop and maintain trade secrets relating to their operations. Identification of specific algorithms and the inputs provided to those algorithms to produce a desired result will differ between manufacturers, and a manufacturer that hits on a constellation of choices that results in superior performance will likely want to keep that from others in the field.
Additive manufacturing, sometimes referred to as “3-D printing,” continues to attract interest and venture capital money despite the accurate decline in the consumer market. Additive manufacturing allows lighter, stronger alloys to be used instead of traditional materials. It also enables a more efficient supply chain in which parts are manufactured when and where they are needed, rather than being manufactured in one place and shipped to another.
Although some accurate developments point to a future in which large, complex items such as entire vehicles can be printed, most current use cases for this technology are to produce parts or subsystems for use in larger systems. The ability to use additive printing technology to manufacture machine parts requires manufacturers to be cognizant of the patent law doctrine of repair and reconstruction, which distinguishes between permissible repair of a patented article and impermissible reconstruction of a patented article, the latter of which is patent infringement. Manufacturers of larger systems will likely want to consult with patent counsel to ensure that their patent coverage is as robust as possible. Similarly, manufacturers of smaller components may require more extensive indemnity provisions in service contracts to shift the risk of patent infringement back to the customer.
Each part manufactured by 3-D printing is represented as a data file that is used by the printer to manufacture the desired object. Manufacturers will want to consider to what extent their data files can be protected by copyright, allowing them to control the ultimate manufacture of the object represented by the data file.
Finally, manufacturers may find themselves able to protect their printing activities using trademark protection. If, for example, a manufacturer has a specific process that allows them to 3-D print a certain material, or finds that objects printed using their process have superior characteristics to parts printed using other processes, that manufacturer may wish to develop a brand strategy around the process, e.g., Printed Using MagicTM.
Smart manufacturing technology holds great promise for manufacturers while posing intellectual property issues with which many traditional manufacturers may be unfamiliar. Manufacturers that are able to identify those issues and capitalize on the opportunities they present will have the advantage in the shift to Industry 4.0.
With food prices rising at the fastest rate in more than a decade, Britons are trying to make their budget go further in the kitchen by buying cheaper frozen and tinned products and supermarket own-label.
Simon Roberts, the Sainsbury’s chief executive, said this week that customers were watching every penny. They are also making more trips but buying less on each visit, and monitoring the price of their shopping to avoid “till shock” when paying at the end.
With the average annual grocery bill on course to rise by almost £400 this year, there is no magic recipe for price rises of this magnitude but there are ways to reduce costs at home. Here, professional chefs, and the people who train them, offer tips on how to cook on a budget – without cutting the quality of your food.
The Conservative MP Lee Anderson caused a furore when he suggested that nutritious meals could be easily cooked for 30p a time.
Felicity Cloake, the Guardian food writer and the author of Red Sauce Brown Sauce, says it is technically possible to make a meal for as little as that – but only if you have the “luxury of time, the cashflow to buy ingredients in bulk to get the best deal, the knowhow and equipment to do so, and those you’re cooking for are so desperate that they’ll eat anything”.
Keeping costs to within a more realistic set limit is possible, though, if you plan ahead carefully. Miguel Barclay, the author of the One Pound Meals range of recipe books, says: “Sit down and plan meals for the whole week. Think of those meals as being connected rather than separate, so you can use the same ingredients in more than one dish, and make a comprehensive shopping list.”
Then, resist any urge to impulse buy. If it’s not on the list, it doesn’t go in the trolley. “There’s no room for improvising with this method,” he says. “You can’t think: ‘Oh look, that pork’s on offer, I’ll buy some,’ because if you do that, you won’t be able to stay within your budget.”
Once you know what you’ll be eating, buy ingredients in the most “whole” form you can, otherwise you will be paying for the processing, even if that’s just trimming off leaves or cutting into pieces. Use your meal plan to ensure nothing gets wasted.
Stella West-Harling, the founder of the Independent Cookery Schools Association and the head of Feeding Devon, part of the Feeding Britain charity network, says roast chicken can make multiple meals: “Boil the chicken for about 30 minutes in a large pan before you roast it,” she says. “Add carrots, celery, and any other vegetables that need using up, to the water. When you drain it, you’ve got chicken stock.”
Use the stock for soup or gravy, or freeze it for future use. Use leftovers from the roast for a pie, risotto, stir-fry or curry. Once you get down to the bones, boil it up again for more stock, combing off the last shreds of chicken.
The same top-to-bottom approach should be taken with vegetables, says Holly Taylor, one of the head chefs at the Brighton restaurant, Kindling. “Using them all up means you get more value for money from the thing that you’ve paid for.”
Broccoli stalks, Taylor says, needn’t be discarded when they are delicious pickled and used as a garnish or in salads. Similarly, Taylor says, make mashed potato by baking the potatoes whole in the oven, cutting them in half and scooping out the fluffy mash, then keep the skins to make loaded potato skins for the next day.
When you pluck a recipe from a cookbook you may end up going out and buying everything on the ingredients list. “You might not use those ingredients again, so they’ll just sit in your cupboard,” says Lewis Walker, the deputy head of the Birmingham College of Food. “It’s not cost effective.”
Planning meals around the same groups of flavours, herbs and spices means you are more likely to use up ingredients. “You might say: ‘Right, we’re going to go through an Italian phase for a little while, so I’m going to use all my Italian ingredients over the next few weeks,’” Walker says. “By not diversifying too much you’re always using what you’ve got in the cupboard rather than buying in more.”
“I don’t think many people realise tomato-based pasta sauces are so cheap and simple to make, or they wouldn’t buy them,” Cloake says. “Some tins of tomatoes, maybe a clove of garlic or some soft herbs, a pinch of sugar and salt, simmered on a low flame for 30 minutes, and you’ve got something much nicer. It freezes brilliantly, so make more than you need and unearth as necessary.”
For comparison (at the time of writing), a 400g tin of plum tomatoes costs 28p in Tesco, while a 350g jar of Loyd Grossman tomato and basil sauce costs £1.90. Even when you take into account the cost of simmering on the hob (between 25p and 50p for an hour’s use, according to the Centre for Sustainable Energy) and the other ingredients, it can still work out cheaper to make your own.
To mop it up, homemade flatbreads and tortillas are hard to beat for value, Barclay says. “I don’t make my own bread but I do make tortillas. They are so quick, simple and cheap – one tortilla comes in at under 1p.” A 1.5kg pack of plain flour is on offer at 45p at Morrisons – with that, plus a little oil and a pinch of salt and some water, you can make 60–70 tortillas. A pack of eight Old El Paso flour tortillas costs £1.45.
A considerable amount of a household’s energy bill is consumed in the kitchen – with 12% coming from cooling or freezing, 4% from cooking and typically 16% from the washing machine and dishwasher, according to the Energy Saving Trust (ETS).
To reduce energy use, Brian Horne, a senior insight and analytics consultant at ETS, says: “Let food cool down completely before putting it in the fridge, and defrost food in the fridge because this will help to keep your appliance cool. Never leave the door open unnecessarily, and keep the temperature between 3C and 5C. Defrosting freezers regularly will maintain efficiency.” If you need to replace your appliance, choose the best energy rating you can afford for longer-term savings.
Induction hobs are the most energy efficient but they are not the cheapest to run. “Gas is cheaper than electricity, so gas hobs typically have the lowest running costs,” Horne says. “And an electric oven will have lower carbon dioxide emissions than a gas oven but it is more expensive to run.”
Whatever the oven type, using it efficiently is the best way to reduce costs. “In the restaurant, we have conversations in the morning about what’s going in the oven, and at what time,” Taylor says. “Basically, working out an oven timetable.”
Adopting a similar approach could save time and money. “Always fill your oven,” Cloake says. “If you’ve got it on anyway for a roast, do extra vegetables to eat throughout the week, or pop in a loaf of soda bread – super simple and minutes to put together.”
Unless you are making something delicate, switch off the oven 15 minutes before it’s done and leave it to finish cooking in the residual heat, she adds.
When reheating those dishes you made in advance, “a microwave is more cost-efficient than a conventional cooker”, Horne says, who adds that boiling water in a kettle is more efficient than a hob.
Optimise hob use, too. “I use the water I’ve had on for steaming vegetables to make gravy for the Sunday roast,” Walker says. “And you can put vegetables into the water you’re using to cook your pasta – just pay attention to the different cooking times they need.”
Top of the list for Taylor is a good knife. “I know it’s chef-y but it will help you to cut down on waste and you’ll have it for life.” An ice cube tray is also handy for an unused bunch of herbs, she says. “Particularly if you live on your own. If you don’t use a whole bunch of herbs, it will sit in your fridge rotting. You can chop them up and cram them into ice cube trays with a little bit of water to use another time.”
For Barclay, the handiest tool in his kitchen is a silicone spatula. “I really use it for everything; cooking scrambled eggs, scraping the last bits of mashed potato from the pan. I even took it with me when I stayed in an Airbnb recently.”
It’s a bigger-ticket item, but an air fryer, which heats and cooks more quickly than a conventional oven, is good if you frequently cook small amounts of food, such as oven chips, or fish fingers, Cloake says. A good hand blender for soups is useful, as are measuring spoons, she says. “And the humble sieve. It does a better job of draining food than your average colander, sifts icing sugar and flour and also works as a steamer. I use mine for so much I have separate sieves for savoury and sweet things.”
A accurate survey by the food waste charity Wrap highlighted the presence of UFOs (unidentified frozen objects) in UK freezers, with more than a third of people saying theirs is sometimes a “total disaster”, making it extremely hard to work out the contents.
“I also try to keep an inventory of the freezer on the door, so I remember what’s in there,” Cloake says. “The same goes for the fridge; clearing it out regularly is a dispiriting job but it stops you wasting food and space, and done weekly, will save you time.”
Walker adds that the golden rule in a restaurant kitchen is to have a “first-in, first-out stock rotation, so whenever you receive a delivery, everything new goes to the back”. Take the same approach with your fridge and store cupboard to avoid forgetting ingredients you could use up in a dish.
Rather than buying in jars of mixed spices, raid your spice rack to see if you already have what you need. Garam masala, for example, needs cardamom, cloves, black peppercorns, cumin, nutmeg and cinnamon. “Mix it as a powder, or cook it off and add oil, and it will be a bit like a Patak’s paste,” West-Harling says. “And if you don’t have all the ingredients, adapt it.”
Growing your own herbs can be simple. If you have outdoor space, mint is a satisfyingly simple to grow. Use fresh leaves through the summer, and dry some for mint tea in the winter months. Jane Perrone, a journalist and the presenter of the On the Ledge podcast, says: “Perennial herbs such as rosemary and thyme need an awful lot of sunshine, so they don’t grow well indoors. But herbs like basil, coriander and parsley, providing you can provide them enough light, can be grown in your kitchen.”
You can raid your spice rack and plant coriander seeds straight from the jar, and if you buy a pot of basil, you can extend its life by teasing out the seedlings and replanting in compost in a larger pot.
Every few months, there seems to be at least one negative story about Interior Designers (IDs) or contractors.
It's unfortunate that the relationship between IDs, contractors, and homeowners tends to be quite adversarial - sometimes it seems like homeowners have to act like guards, waiting to "catch" the ID doing something wrong.
But an email from a reader got us thinking: perhaps the relationship needs to be more of a two-way street:
We recently had an email from S, a reader who had issues with her own ID. She and her husband actually engaged their previous ID, even though he wasn't the cheapest.
Still, they did encounter issues that were quite puzzling. "There were some instances where we were left scratching our heads and wondering what happened," S recounted. They were renting a place while waiting for the renovation to be completed, and before the reno started, they were promised that the place would be finished by February.
In December, they were informed that February was no longer possible, and now it would most probably be the end of March. As a result, they had to pay for an additional month of rental.
There were additional hiccups too. Their previous place had full-length wardrobes (2.8m high), and they assumed that their new place would be based on full-length wardrobes too. As a result, they had to go through "another back and forth with our ID because the 3d renderings did show full-length wardrobes."
Despite these issues, however, S seemed to be quite tolerant.
She noted that "IDs and sub-cons need to make money too. Squeezing them out of business doesn't do anyone any good."
There was a case where "he gave us a quote for vinyl flooring which turned out to be >1k more than the quote we got from another vendor outside".
But she was understandable about the situation for IDs too and they ended up sticking with their ID as she fairly pointed out, "his margins elsewhere are probably squeezed (he said so too eventually) and he needs to make money somewhere".
Ultimately, S wanted to highlight that "there is a lot of provide and take in every good working relationship".
And so while there were not-so-good parts, there were many positive aspects that they had to account for as well.
For instance, "he didn't charge us extra when we said we wanted drawers in a portion of our wet/dry kitchen when the quote was based on cabinets". And "he absorbed the extra cost to fabricate full-length cabinets" too.
She did acknowledge that he was probably getting better profit margins from them as compared to other clients who may have gone through every single line item to compare - but being understanding in that regard helped when it came to points that they overlooked too.
As S pointed out, one good way to prevent further comparisons would be to "stop listening to your friends/uncles/aunties/friends pet dog/cat who swear that they got a better deal than you when they/their distant cousin 10x removed just did their renovation."
As such, most of the real estate industry tends to be on the side of the homeowner (and rightly so, at times); and even some developers are notoriously harsh on contractors. But is there a way to deal fairly with IDs and contractors, for the benefit of both parties? Here are a few things we could consider:
Some homeowners think aesthetic elements, like design themes, colours, and styles, are not really worth paying for. It's a little bizarre how Singaporeans will, on the one hand, admire great design and say "wow, I don't have this kind of talent", but at the same time decide it's not worth paying someone for it.
The aesthetic element is about much more than just "tying a room together". Some designers have spent significant amounts of time, and money, in school to understand the origins of certain styles, and the subtle elements that make them work.
Shabby chic, for instance, came from makeshift efforts to uplift the home in the destruction of the Second World War, hence the use of furniture from a specific era; and Art Deco styles require a certain repetition of patterns to work. Whole volumes can, and have, been written about these.
It is, of course, the customer's prerogative to ignore all this, and go for a pure contractor (i.e., someone who just executes what you want); that is a way to save money. But it isn't fair to dismiss this element of an ID's work as being somehow worthless.
Also, if you do pay for an ID, at least hear them out on the fundamentals of a given style.
A common habit of Singaporean homeowners is to dig up a cheaper electrician, source of tiles, etc., and demand their ID use them. These are the scenarios where the homeowner says "My aunt used this carpenter and he charges much less, and she says he's good."
When the ID objects, some homeowners jump to the conclusion that it's due to kickbacks, conspiracies, etc.
But take the ID's perspective for a moment: what happens if they're forced to use a cheaper carpenter, and his cabinets sag? Or what if they're forced to use cheaper tiles, and they break?
Quite often, the homeowner will still blame the ID. This is why they might push back: the designer can be held liable for certain failures, even when the sub-contractor or materials are the ones their customer insisted upon.
Another factor here is urgency. If you're demanding that the ID complete things on a tight time schedule, it's unfair to then foist an unfamiliar sub-contractor or supplier on them.
They've chosen their suppliers or sub-contractors with your time limit in mind, and they're working with people whom they think have the best chance of delivering. If you change these people, and it causes a delay, it's not really fair to blame the ID.
Describing interior architecture is like trying to write instructions on how to tie a shoelace - it's an exercise in frustration, and the odds of being misunderstood on at least one point is going to be close to 100 per cent.
Singaporean homeowners do need to be a little more understanding when their ID misinterprets something. There's a high chance this will happen even if there are loads of e-mails, text messages, and even sketches.
Scale is the biggest issue here: unless you're in the industry yourself, it can be hard to visualise the size and space even with 3D art - that's why some owners end up complaining that their rooms seem smaller than expected, after a partition.
There's also the simple issue of language. Not all IDs, just like not all homeowners, communicate well; or in some cases, both parties are proficient in different languages. These situations call for more patience - and both sides should brace themselves for potential miscommunications.
This isn't to say we shouldn't take every precaution, to verify each step before it's put in place - just that, when things do go wrong, we should consider that the miscommunication may be no one's fault.
Some homeowners have the mistaken idea that these sketches and renderings are just churned out in minutes, by computers. They're not. Some of these things can take hours to do, and whole teams are sometimes involved.
But when an ID firm takes "too long" to get back to a customer with these, they'll say the designer is slow or uninterested. We've also heard of homeowners who demand a ridiculous number of these, with one revision on top of another; and are then furious when the designer says it's too much to comply with.
Singaporean homeowners blame the ID for every delay, and this is probably the height of unfairness. The simple fact is, that no designer - however professional or skilled - is in full control of the time frame.
This is because a lot of people are involved in a renovation project. The suppliers may find their shipment delayed. The movers may accidentally break things. The carpenter may be down with Covid. The electrician may have a family emergency and have to go back to Malaysia for a week.
There are a virtually unlimited number of things that can go wrong when you factor in the sheer number of people involved.
While designers should still have milestones and be held to certain time frames, it's not always fair to pin the entire blame on them. It might be better if we ask what they're doing to deal with a delay, and gauge their adequacy from that.
Some homeowners will insist the designer change key elements or pick sub-contractors and vendors, based on recommendations from their relative/friend. This often ignores the fact that the designer is a qualified expert, and may know better than your relatives or friends.
Do bear in mind that every single home is different. What worked for your aunt's place may be a bad idea for yours, and it's possible the "super cheap" tiles your friend found are imitations (but they don't know it yet).
This isn't to say you should discount the advice of people you know; just that you shouldn't immediately prioritise it over the advice of your ID.
This attitude is also a way to do yourself an injustice. If you're paying for a designer's services, why fight so hard to contradict them? That's like hiring a cleaner and then insisting on doing the cleaning yourself.
If a friend or relative can suggest something better, do bring it up to your designer for discussion; but don't immediately text them and insist they do as you're advised.
What do you think are some ways homeowners and IDs can find more common ground? Do let us know your ideas. In the meantime, you can find more strategies and tips for homeowners on Stacked. We'll also provide you with in-depth reviews of new and resale developments alike.
This article was first published in Stackedhomes.