IBM is officially partnering with the Massachusetts Institute of Technology (MIT) to run an artificial intelligence (AI) research lab. This Watson-branded joint MIT-IBM AI research initiative — a partnership of acronyms — will be funded through a 10-year, $240 million investment from IBM and will be co-located at IBM's Research Lab in Cambridge and at the MIT campus.
According to an IBM press release, the MIT-IBM Watson AI Lab will be one the largest long-term AI collaborations between a university and a member of the tech industry.
Its goal is to enable more than 100 scientists, professors, and students to pursue research focused on such areas as the development of AI algorithms that could expand machine learning capabilities, the improvement of AI hardware, the exploration of AI's economic and societal benefits, and the identification of AI applications in key industries.
The team at IBM knows that despite all the exact attention given to what AI can do, there's still so much that it can't.
“The field of artificial intelligence has experienced incredible growth and progress over the past decade," IBM's senior VP for Cognitive Solutions and Research John Kelly III said in the press release. "Yet today’s AI systems, as remarkable as they are, will require new innovations to tackle increasingly difficult real-world problems to Strengthen our work and lives."
IBM and MIT want to work together on those innovations, and their partnership might be exactly what's needed to put AI's promise to use in the real world. IBM has already illustrated the potential for their Watson AI to Strengthen healthcare, and with MIT's help, they may be able to take the technology even further in that field and so many others.
“True breakthroughs are often the result of fresh thinking inspired by new kinds of research teams," asserted MIT President L. Rafael Reif in the IBM press release. "The combined MIT and IBM talent dedicated to this new effort will bring formidable power to a field with staggering potential to advance knowledge and help solve important challenges.”
This article is part of a VB special issue. Read the full series here: How Data Privacy Is Transforming Marketing.
Text-to-image AI systems such as DALL-E 2, Imagen and Midjourney are growing in popularity and capability right now, offering creators a revolutionary new way to produce content.
Generating images from text prompts is a radical new approach to art-making and creative expression. But it also gives us the first glimpse of a fundamental shift in how we can better communicate and collaborate with our machines. And it is this underlying innovation in human-computer interaction that will disrupt the near-future possibilities for how we are able to work and play.
Mediamorphosis is a term coined in the 1990s by Roger F. Fidler to describe a conceptual framework for “a unified way of thinking about the technological evolution of communication media.”
Three decades later, we have entered an era when a literal mediamorphosis, or metamorphosis of media, is possible, where one medium can be transformed and transmuted into another in minutes with AI tools. In this new production process any input data can become sound, image or text which in turn can be recombined to generate further sounds, images or text. You will soon be able to choose your preferred point of entry and can get things started by humming a tune, drawing a picture, selecting a spreadsheet or even just moving your arms or eyes.
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In December 2021 Meta demonstrated a prototype system for using AI to automatically animate children’s drawings and in July of this year showcased Make A Scene, its new exploratory AI research concept for empowering anyone to bring their imagination to life by using text prompts and simple sketches.
According to MetaAI Research, “To realize AI’s potential to push creative expression forward, people should be able to shape and control the content a system generates. It should be intuitive and easy to use so people can leverage whatever modes of expression work best for them, whether speech, text, gestures, eye movements, or even sketches, to bring their vision to life in whatever mediums work best for them, including audio, images, animations, video, and 3D.”
We are fast approaching a period that will be characterized by the rapid democratization of creative media production. The best way to strengthen the top of a pyramid is to widen its base.
In August, short-form video giant TikTok added a rudimentary AI greenscreen effect for creating background images, making a text-to-image process publicly available to over a billion people globally.
The creator economy is already booming. It’s become an estimated $100 billion industry over the last few years. Of the 50 million creators worldwide, 46 million are amateur creators (92%) and four million are professional creators (8%). Those amateurs will be attracted by new business models that allow opportunities for them to get paid.
What has already happened to photography, with tools like Instagram and now the new AI image generators, will soon begin to happen across other disciplines such as design, animation, video, architecture, art, music and data. Critics are sounding alarms about how all of this will impact society. Fred Ritchin suggests that the virtual world “will enlarge to compensate for a lack of agency in the physical one.”
Photography didn’t kill painting and Instagram didn’t kill photography. A world where millions or even billions of people are co-creating with AI to produce extremely high-quality and low-cost rich media content in a variety of disciplines is a very different world from where we are now. But how will it affect our day jobs?
Marketing teams will need to upskill their tools and workflows to reach customers on increasingly saturated multimedia platforms and try to either compete against or collaborate with the synthetic beings who will soon populate them in astonishing numbers. Data presentation and reports will need to be reimagined.
We are already moving towards a screenless future in which more than 50% of the global population has adopted voice technology, whether it’s inside their cars, on their smartphones, or, more evidently, with their smart speakers. The popularity of social audio app Clubhouse has led to the launch of Twitter Spaces and the relaunch of Facebook Live Audio Rooms.
Data researchers, communicators, educators and policymakers need to think beyond silent graphical visualizations to interact with audiences across new channels in ways that can extend impact, reach and engagement, or decide to abandon them altogether to the purveyors of misinformation. And corporate reporting needs to adopt a more interesting format than the 100-page PDF.
Attracting talent is harder than ever, and companies need to define a strong employee value proposition to stand out and attract the right candidates. The widely used metaphor of an organization operating like a machine has propagated a culture that treats employees like machines and coaxes individuals to act like machines to fit in. This new paradigm offers an opportunity to start conversations about how we could bring the best of our human creativity, imagination and compassion to work in tandem with machine intelligence.
The corporate world paid attention to AI just before the turn of the century when IBM’s supercomputer Deep Blue famously beat world chess champion Garry Kasparov in 1997. The match has been the subject of books and films but it wasn’t the end of the story. After his very public defeat, Kasparov had the idea to invent a new form of chess in which humans and computers cooperate instead of competing with each other. Kasparov named this form of chess “advanced chess” as it increased the level of play to heights never before seen by combining the qualities and the beauty of both perfect tactical play and highly meaningful strategic plans.
We have been accustomed to thinking that we must spend long years learning how to operate our software applications. Our outlook has been limited by focusing mostly on mastering the skills required to communicate effectively with our poorly designed tools.
We are rapidly entering an age when our tools understand us and can move beyond simply understanding what we want them to create to being able to co-create with us. We need to shift our focus and open our imagination. And all of that starts with meaningful conversations.
Hugh McGrory is a media artist and co-founder at Sonify.
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Renowned futurist Thomas Frey is quoted as saying, "If you change your vision of the future, you will also change the way you make decisions today." Frey, whose insights into the field of futurology have caught the attention of global companies like Google, IBM and AT&T, shared a similar sentiment last week at this year's Innovation, Opportunity and Investment (iOi) Summit, where he discussed the future of real estate.
Organized by the National Association of Realtors (NAR), the annual conference welcomed professionals from all over the country to explore the latest groundbreaking ideas in the world of PropTech.
Topics ranged widely from non-fungible tokens (NFTs) to affordable housing to insurance, with presenters that included former Zillow CEO Spencer Rascoff, former Realtor.com CEO Ryan O'Hara and Million Dollar Listing: New York star Fredrik Eklund taking the stage to champion innovation over adaptation.
EQTY CEO Mike Shapiro, who led a discussion on correlative behaviors between asset classes and residential real estate, said one of the main takeaways from the conference was that "the real estate business is looking for an evolution to bring them forward. They're looking for tools and efficiencies to drop costs and increase transactional volume."
Couldn't make it to the event? Here are three big ideas to come out of this year's iOi Summit.
Behind the scenes, artificial intelligence (AI) is revolutionizing the process by which industries gather and analyze data, and real estate is no different. Algorithms can go through millions of public documents in seconds, looking through property values, debt levels and home renovations to best match buyers with the right home and mortgage.
With the vast majority of listings living on some form of a digital platform, the work of collecting and monitoring data can become more efficient than ever, thanks to machine learning (ML).
From photos alone, automated image analysis like Amazon's Rekognition can extract and organize information on a property, such as the presence of a fireplace, swimming pool or French doors—data that can be used to understand consumer trends or best sort the listing online. For homebuyers, this would mean more accurate and specialized searches.
In addition to data extraction, these image and video analysis services can also detect and moderate unwanted content, providing a moderating tool for online platforms. Moderation tools are handy for real estate platforms featuring user-generated content.
Mortgage processing can also be made quicker and easier with the support of AI, which can detect errors instantly and verify the information to prevent fraud.
As many agents will tell you, half the battle when closing a property is getting clients to step inside and look around for themselves. Now, with advancements in virtual reality (VR), buyers can see a property without ever having to their front door.
Fueled partly by early pandemic lockdowns and social distancing, the need for virtual replicas has remained a priority for commercial and residential developers and brokers who are just beginning to unlock this burgeoning technology's potential.
Known as a digital twin, this immersive 3D model allows homebuyers to interact with a property, including those that have yet to be built.
While the concept is nothing new, exact advancements have been significant, with sophisticated digital twin technology now more accessible and affordable than ever.
Companies like Matterport, a 3D media startup, are bringing products to the market that will allow brokerages of all sizes to utilize this increasingly popular technology. Products include 3D scanning and 360-degree cameras as well as motorized mounts that enable 3D scanning with a mobile.
For developers, digital twins could allow for the most cost-effective and eco-friendly means of construction and early detection of previously unforeseen problems.
Real Estate Indexing
In previous and present schools of thought, real estate has often been treated as separate from other asset classes. However, some experts, such as Mike Shapiro, argue this shouldn't be the case.
Instead, Shapiro asserted a counterpoint that, by understanding correlations between residential real estate and asset classes, such as equities, pricing could be more accurately understood and thus the market as a whole.
Just as publicly traded companies are indexed, real estate can be organized into various silos, which can then be used in investing, predicting or hedging, Shapiro shared.
For example, the Dow Jones Industrial Average, which reflects the 30 most prominent companies listed on U.S. stock exchanges, can be compared to premier communities like Beverly Hills or Manhattan. In the same line of thinking, the heavy growth associated with stocks listed on the NASDAQ composite can be tied to markets like Austin or Nashville.
With these correlations in mind, insurance companies, appraisers, banks, and real estate agents may well have a more prescient understanding of pricing.
Submitted by IBM
WASHINGTON, October 14, 2022 /CSRwire/ - The Hispanic Heritage Foundation (HHF) announced today its collaboration with IBM (NYSE: IBM) which includes leveraging IBM SkillsBuild – a free education program that helps students and adult learners develop valuable new skills and access career opportunities in technology fields – by providing digital content, personalized mentoring, and the experiential learning they need to gain technical, critical thinking, and creative problem-solving skills. The program will be offered for FREE to HHF Network, is completely digital, and includes IBM-branded digital credentials that are recognized by the market to create direct pathways to tech jobs. The effort will be open to high school students, college students, young professionals, and adult learners.
“This IBM SkillsBuild collaboration has been a transformational goal of our tech pathways strategy and goal for years,” said Jose Antonio Tijerino, President, and CEO of HHF. “Our community has a tremendous value proposition for America’s workforce and through this innovative collaboration, America can benefit from the talent we have always had to offer. Our collective mission is to provide training and opportunities for our community to make an impact in the tech sector.
We are grateful to IBM for allowing us to leverage their expertise and pathways in preparing the Latinx community for jobs that desperately need to be filled. As Latinos, we’re ready as we always have been.”
The learning pathways available through IBM SkillsBuild include courses on workplace skills, such as communication and leadership skills designed for any beneficiary wishing to understand how to work in the digital world, as well as courses on data analytics, cybersecurity, cloud computing, and many other technical disciplines. The program will also help early school leavers and long-term unemployed to gain what is required to re-enter the workforce. Courses are available in English and Spanish, providing Hispanic learners with a better and deeper understanding of course materials, to help ensure completion and professional competency.
“As a Latina, I am very excited and honored to be partnering with the Hispanic Heritage Foundation to provide free education and career readiness resources to Hispanics nationwide,” said Claudia Cortes Romanelli, Director of Corporate Social Responsibility at IBM. “I see every day the great opportunity to invest in skilling the next generation of STEM talent from the Hispanic community. We look forward to working with HHF as part of our commitment to equitably skill 30 million people worldwide.”
The Hispanic Heritage Foundation award-winning LOFT (Latinos on Fast Track) program is a leadership and workforce development program and network with a focus on various sectors or “tracks,” including tech. HHF’s broad network and beyond will be exposed to IBM SkillsBuild to learn, and build skills in artificial intelligence, data science, cloud, security, information technology, and more, with opportunities for mentoring and networking in the tech space as well as earning certifications and placements into the workforce.
IBM and HHF’s collaboration is part of IBM’s commitment to equitably skill 30 million people globally by 2030.
About the Hispanic Heritage Foundation
HHF’s mission focuses on education, the workforce, identity, and social impact through the lens of leadership and culture. For more information, visit www.hispanicheritage.org and follow the Hispanic Heritage Foundation on Instagram, Facebook, Twitter, and TikTok
About IBM Education
As part of the company's Corporate Social Responsibility efforts, IBM's education portfolio takes a personalized, diverse, and deep approach to STEM career readiness. IBM's pro bono programs range from education and support for teens at public schools and universities to career readiness resources for aspiring professionals and job seekers. IBM believes that education is best achieved through the collaboration of the public, private, and not-for-profit sectors.
IBM SkillsBuild is a free education program focused on underrepresented communities, that helps adult learners, and high school and university students and faculty, develop valuable new skills and access career opportunities. The program includes an online platform that is complemented by customized practical learning experiences delivered in collaboration with a global network of partners. The online platform offers over 1,000 courses in 19 languages on cybersecurity, data analysis, cloud computing, and many other technical disciplines — as well as in workplace skills such as Design Thinking. Most importantly, participants can earn IBM-branded digital credentials recognized by the market. The customized practical learning experiences could include project-based learning, expert conversations with IBM volunteers and mentors, premium content, specialized support, connection with career opportunities, and access to IBM software. IBM SkillsBuild operates in 168 counties and has supported 2.2M learners.
Innovation – joining invention and insight to produce important, new value – is at the heart of what we are as a company. And, today, IBM is leading an evolution in corporate citizenship by contributing innovative solutions and strategies that will help transform and empower our global communities.
Our diverse and sustained programs support education, workforce development, arts and culture, and communities in need through targeted grants of technology and project funds. To learn more about our work in the context of IBM's broader corporate responsibility efforts, please visit Innovations in Corporate Responsibility.
More from IBM
Artificial intelligence, better known as AI, sounds like something out of a science-fiction movie. It brings to mind self-aware computers and human-like robots that walk among us. And while those things are part of the overarching artificial intelligence definition and may exist in the future, AI is already a big part of our everyday lives. So, what is artificial intelligence, exactly? It’s complicated, but every time you use Siri or Alexa, you’re using AI, and that’s just the beginning of its practical applications.
“The main benefit of AI is that it can bridge the gap between humans and technology,” says AI researcher Robb Wilson. “AI will allow everyone to communicate with computers the same ways they communicate with other humans: through speech and text. This can have the massive benefit of putting the problem-solving capabilities of powerful technology in everyone’s pocket.”
If you’re curious about AI, you’re not alone. In a recent Reader’s Digest survey, 23% of respondents said they were interested in learning more about it. It’s an important Topic because the future of AI will shape everything from the internet to medical technology to our workplaces—for better and for worse. While AI will open up a whole new world with real robots helping in ways you probably never imagined, we’ll also have to contend with a changing job market, as well as unintended AI bias. We spoke to technology experts to break it all down. Here’s what you need to know.
In a nutshell, artificial intelligence is simply a machine that can mimic a human’s learning, reasoning, perception, problem-solving and language usage. An AI computer is programmed to “think,” and this process hinges on programming that is called machine learning (ML) and deep learning (DL).
With ML and DL, a computer is able to take what it has learned and build upon it with little to no human intervention. But there are a few key differences between the two. In machine learning, a computer can adapt to new situations without human intervention, like when Siri remembers your music preference and uses it to suggest new music. Deep learning, on the other hand, is a subset of machine learning inspired by the structure of the human brain, says Lou Bachenheimer, PhD, CTO of the Americas with SS&C Blue Prism, a global leader in intelligent automation. As you may have guessed, this helps it to “think” more like a person.
Essentially, machine learning uses parameters based on descriptions of data, whereas deep learning also uses data that it already knows. In a real-world application, deep learning might help a digital worker easily decipher and understand handwriting by learning a variety of writing patterns and comparing it with data about how letters should look. AI will also play a big role in the metaverse in the future.
In 1935, Alan Turing envisioned machines with memory that could scan that memory for information. That idea eventually spawned the first digital computers, and in 1950, Turing developed a method to assess whether a computer is intelligent. The Turing Test involves asking a number of questions and then determining if the person responding is a human or a computer. If the computer fools enough people, it is considered thinking or intelligent.
It wasn’t until 1955, however, that scientist John McCarthy coined the term “AI” while writing up a proposal for a summer research conference. McCarthy later became the founding director of the Stanford Artificial Intelligence Laboratory, which was responsible for the creation of LISP, the second-oldest programming language and the one primarily used for AI.
Today, we have all kinds of “thinking” computers and robots. Have any passed the Turing Test? Yes. In fact, a chatbot recently fooled a panel of judges into thinking it was a 13-year-old boy named Eugene Goostman. Google AI has also passed the test. Does that mean these computers are sentient beings? No. Many say that the Turing Test is outdated and needs to be revised as a way to determine if a computer is actually thinking like a human. Currently, no computer actually thinks like a human.
This essentially boils down to how AI learns, and it’s a lot like how a parent might teach a child. “When it was young and immature, AI was trained using lots of rules and patterns, which made systems like IBM’s Deep Blue really good at chess,” says Wilson about the program that was able to beat grand master Garry Kasparov in a chess match in 1997. “As AI has matured, it’s been trained more through trial and error. The AI makes mistakes, and like a parent, humans provide it with course correction and necessary context. As AI gets better at certain things, some of the rules established early on can be removed (much like a child earning more independence), creating further opportunities for growth.”
Of course, it doesn’t have a human brain’s neurons. Instead, a computer uses programming given to it by a human, or its algorithms process data to learn.
AI’s ability to get smarter over time makes it capable of producing solutions for previously unsolvable or challenging problems, according to Beena Ammanath, leader of Technology Trust Ethics at Global Deloitte and author of the business guide Trustworthy AI. For example, AI can learn to see connections in data sets that are way too complex for humans. This can lead to innovations like engineering better traffic flow in cities or predicting health problems in large demographics of people, and it can work with virtual reality to create digital models and other immersive experiences.
Yuichiro Chino/Getty Images
Artificial intelligence comprises four different types of AI. These types are then subdivided into two distinct groups called strong and weak AI.
The four types of AI are reactive machines, limited memory machines, theory of mind machines and self-aware AI. Each is progressively more complex and gets just a little closer to being like the human mind.
Strong and weak AI are separated by how “smart” the AI has become. With strong AI (also known as artificial general intelligence or AGI), a machine thinks like a human. Weak AI, or narrow AI, is the dumber version—and the one we currently have. Experts are split on when we will achieve strong AI. Many experts believe that it could happen within the next 50 years, though some say there’s a small chance that it could happen in the next decade.
With strong AI, a computer could learn, empathize and adapt while performing many tasks. It could be used to create robot doctors or many other professions that take both emotional intelligence and technical ability that grows and evolves as the robot learns through experiences. This is similar to personal health-care companion Baymax in the movie Big Hero 6 or the public servant robots in the movie I, Robot.
Weak AI enables the machine to do a task with the help of humans. Humans are needed to “teach” the AI and to set parameters and guidelines on how the AI should respond to perform its tasks. Siri, Alexa, Google Assistant, self-driving cars, chatbots and search engines are all considered weak AI.
Now that you know the answer to the question “What is artificial intelligence?” you might be wondering where it is. The fact of the matter is that AI is everywhere in our world. Here are just a few common ways you interact with it on a daily basis without even realizing it.
One of the most famous examples of early AI was the chess computer we noted earlier, Deep Blue. In 1997, the computer was able to think much like a human chess player and beat chess grand master Garry Kasparov. This artificial intelligence technology has since progressed to what we now see in Xboxes, PlayStations and computer games. When you’re playing against an opponent in a game, AI is running that character to anticipate your moves and react. If you’re a gamer, you’ll definitely be interested in the difference between AR and VR—and how AI relates to both.
Another example of artificial intelligence is collision correction in cars and self-driving vehicles. The AI anticipates what other drivers will do and reacts to avoid collisions using sensors and cameras as the computer’s eyes. While current self-driving cars still need humans at the ready in case of trouble, in the future you may be able to sleep while your vehicle gets you from point A to point B. Fully autonomous cars have already been created, but they are not currently available for purchase due to the need for further testing.
Currently, doctors are using artificial intelligence in health care to detect tumors at a better success rate than human radiologists, according to a paper published by the Royal College of Physicians in 2019. Robots are also being used to assist doctors in performing surgeries. For example, AI can warn a surgeon that they are about to puncture an artery accidentally, as well as perform minimally invasive surgery and subsequently prevent hand tremors by doctors.
Plus, robots come in handy when organizing clinical trials. AI can pick out possible candidates much more quickly than humans by scanning applications for the right ages, sex, symptoms and more. They can also input and organize data about the candidates, trial results and other information quickly.
Don’t want to pay more? AI can help. “The insurance company Lemonade is a good example,” says Wilson. “They’re relative newcomers to the space but have already disrupted the business model used by old-guard insurance giants. Users have easy access to policies and policy information through an intelligent bot, Maya, who continually receives rave reviews from customers.” Lemonade claims their customers save up to 80% on their insurance costs with a paperwork-free signup process that takes less than 90 seconds.
Similarly, China’s Ant Group has upended the global banking industry by using AI to handle their data and deal with customers. “As the 2020s were about to dawn, Ant surpassed the number of customers served by today’s largest U.S. banks by more than 10 times—a stat that’s even more impressive when you consider that this success came before their fifth year in business,” notes Wilson.
One survey from 2018 found that 60% of the companies surveyed were using AI-enhanced software in their businesses. A few short years later, AI is everywhere in the workplace. From search engines to virtual assistants, and from plagiarism detectors to smart credit and fraud detection, there’s probably not an industry that doesn’t use some form of AI technology.
Though it’s hard to predict just how AI will be used in the future of work, it is already making the workplace more enjoyable and efficient by taking over more mundane tasks like data processing and entry. In a 2022 study by SnapLogic, 61% of workers surveyed said that AI helps them create a better home/life balance, and 61% believed that AI made work processes more efficient.
The Industrial Revolution created machines that amplified the power of our bodies to move and shape things. The Information Revolution created computers that could process enormous amounts of data and make calculations blindingly fast. AI is performing dynocognesis, which is the process of applying power to thinking, explains Peter Scott, author of Artificial Intelligence and You and founder of Next Wave Institute, an international educational organization that teaches how to understand and leverage AI.
By essentially being a heavy-lifting machine for thought, AI has the power to advance industries like health care, medicine, manufacturing, edge computing, financial services and engineering. “With the right set of tools and diverse AI, we can harness the power of the human-to-machine connection and build models that learn as we do, but even better,” says Ammanath. It can also enhance the performance of 3D printing, not to mention eliminate human error in the process.
According to Ammanath, some benefits of AI include:
Of course, some problems have popped up as we venture into this new territory. For starters, as AI capabilities accelerate, regulators and monitors may struggle to keep up, potentially slowing advancements and setting back the industry. AI bias may also creep into important processes, such as training or coding, which can discriminate against a certain class, gender or race.
“Overall, the tool using AI and its ethical implications or risks are going to depend on how it is being used,” says Ammanath. “There is no single set of procedures that define trustworthy AI, but valuable systems should be put into practice at each institution to prevent the risks of AI development and utilization, as well as to actively encourage AI to adapt as the world and customer demands change.”
Another barrier to AI is the fear that future robots with AI will take away jobs. Of course, just like with any other automation advancement, new jobs have been created to Strengthen and maintain automations. According to research by Zippia, AI could create 58 million artificial intelligence jobs and generate $15.7 trillion for the economy by 2030.
Some jobs will be lost, though. According to that same research, AI may make 375 million jobs obsolete over the next decade. We’re already seeing some jobs disappear. For example, toll booths that were once run by humans have been replaced with AI that can scan license plates and mail out toll bills to drivers. And travel sites run by AI that can find you the best flight or hotel for your needs have almost completely obliterated the need for travel agents.
The biggest problem lies in the fact that newer jobs created by AI will be more technical. Those unable to do more technical work due to lack of training or disabilities could be left with fewer job opportunities.
As AI progresses, many scientists envision artificial intelligence technology that closely mimics the human mind, thanks to current research into how the human brain works. The focus will be on creating more innovative, useful AI that is affordable.
Ethical AI creation will also be an important part of future AI development. “People are concerned about ethical risks for their AI initiatives,” says Ammanath. “Companies are developing artificial intelligence boards to drive ethical behavior and innovation, and some are working with external parties to take the lead on instigating best practices.” This guidance will ensure that remedies for issues like AI bias will be put into place.
Will the world ever have self-aware AI? Experts are split on this one. Some say that with current innovations, we might one day see a machine that feels and has real empathy. Others say that consciousness is something only biological brains can achieve. For this level of AI, only time will tell.
Now that you know the ins and outs of artificial intelligence, learn about Web3 and how it will affect the future of the internet.
For years, economists and researchers have been predicting how automation would eliminate significant numbers of jobs. Certainly, routine work—jobs largely based on the performance of regular tasks at certain times or for specific situations—has been reduced. However, non-routine work—jobs comprised of tasks performed at irregular intervals and often executed in different ways dependent on the situation—has exploded. This trend is creating better jobs, higher-paying jobs and jobs that require new skills.
For example, consider the automation of self-service retail, mobile phone orders and in-store kiosks. These automation tools did eliminate traditional call center and order-taking jobs. But the high volume of new transactions has created new jobs in service delivery, customer service and support, analytics, supply chain and logistics. Most of these jobs fall into the category of non-routine work.
Facebook (Meta), Amazon and Google now employ thousands of people to curate content, moderate social media and determine how their systems will behave. The need to “train” and “moderate” and “improve” intelligent machines is higher than ever, and these jobs are vital and pay well.
The National Bureau of Economic Research has extensively studied the shift from routine to non-routine work and confirms its significance. Not only have “occupations” shifted, but the growth rate in non-routine jobs is almost 25 times higher than the growth rate in routine jobs (based on data from 1976 through 2014).
What are these non-routine jobs?
Non-routine jobs are typically defined as “service” jobs; they fall into two overlapping categories. The Bureau of Labor Statistics publishes a long list of service-providing industry segments encompassing a wide range of jobs in healthcare, finance, education, trade, leisure, hospitality and professional services. These jobs make up more than 70% of U.S. employment; their average wages have been growing at more than 5% per year. A second, even more important category encompasses jobs that require skills in design thinking, communication, empathy, teamwork and time management. Most jobs in the U.S. fall into this category in one way or another.
Sales is a good example of the shift from routine to non-routine work. While companies like Salesforce, Hubspot, Seismic and Gong are automating sales and marketing processes, the demand for salespeople has gone through the roof. Today, according to Lightcast’s latest data, there are more than 580,000 open sales jobs.
This demand is not due to economic growth; job openings are a direct result of sales automation. Thanks to CRM tools, a salesperson today spends time pouring through Salesforce, looking for qualified leads, then crafting interesting emails or phone conversations to get the attention of prospects. Has sales been automated? Not really. It has been augmented and improved by automation, but it’s still a person-to-person job.
Look at roles like nursing (with the largest number of job openings in the U.S.), management (600,000 open jobs), customer service (245,000 open jobs), and even food service and hospitality (more than 275,000 open jobs). None of these jobs has been automated away; rather, they’ve grown in demand as more and more routine tasks become automated.
Technical skills vs. power skills
Despite the ongoing demand for scientists, engineers and technical professionals, the research also shows that technical careers, while critical and vital to our economy, are also turning into services jobs.
Recent research from IBM found that CEOs don’t only want employees with technical skills, they are also desperately looking for people who are creative, can solve complex problems, manage large teams of people and deal with strategy, time management and organizational growth. Technical salaries do go up with specialization, but almost every study of pay shows that managerial roles pay 50% to 100% more, even in highly technical domains. Yes, it’s hard to hire the world’s best scientists and engineers, but try being the manager of these brilliant people. That is a really tough job.
And this leads me to an important point: We are becoming an economy based on power skills. While technical skills are certainly valuable, skills in design thinking, agility and flexibility, communications, empathy and management are even more so. The top skill requirement on LinkedIn isn’t computer programming or data analytics, it’s communications. And this makes sense. If you can’t listen and communicate your thoughts well, there aren’t many jobs you can really do.
What this means for HR and business
First, we have to expand focus beyond technical skills in training, development and recruitment. You should define these skills, continually develop them and reward them.
Long-term business success and economic growth is now dependent on the ability to understand this shift. An interesting study conducted by the IZA Institute of Labor Economics found that the slowest-growing economies had a much larger percentage of jobs with “routine-intensive roles.” In other words, if you don’t design and engineer jobs to make the shift to non-routine work, your company will suffer, as will the overall economy.
Low unemployment may be here to stay. While historically, companies have laid off workers when the economy slows, that formula seems to be changing. Why? We are constantly reinventing work and creating new jobs as other become obsolete. The fertility and marriage rates are low, and this demographic drought is creating a limited supply of potential employees. So, jobs will continue to be hard to fill.
Our new Global Workforce Intelligence study details how this shift is impacting healthcare workforces right now. The healthcare providers thriving in this difficult time are those re-engineering work, automating the routine tasks in their facilities and taking innovative approaches to train and develop people.
As I see it, the future is not a world in which technology replaces people, but rather one in which jobs continuously get better. Leveraging this trend is key to growth and even survival in the future.
This year marks PCMag’s 40th anniversary, and we’ve got loads of great stories celebrating our nostalgia—an illustrated timeline, an “ode to PCMag” by long-time Editor-in-Chief Michael Miller, an interview with Bill Gates, and many more. Another legacy is our faithful crowd of longtime readers, many of whom have contacted us with kudos and reminiscences.
One such reader is also—just maybe—the original tech influencer. Brian Dewey has had a long career in technology: He’s retired now but worked for IBM Global Services, Time Warner Cable, and myriad other companies. In 1987, five years after PCMag was founded, young Brian was employed by MONY Financial Services. His title? “PC Brand Specifier.”
Brian Dewey’s employer and job title are both easy to find by paging through the May 31, 1988 print issue of PC Magazine, since he was the star of a house ad for the magazine. “It’s my job to select brands of personal computers for 1,500 end users here at MONY Financial services,” Brian is quoted in the ad. “No matter how tough or confusing it gets, my boss expects me to be right every time…That’s why I have to read PC Magazine.”
Brian reached out via email to us: “I was reading…your latest 40th-anniversary celebration article regarding computer stores of the ’70s and ’80s and I remembered I did a commercial for you back in 1988…’
How cool is that?! We thought we’d see if we could get Brian to talk to us a bit about technology’s history and his own. And the man can tell a story.
PCMag: How did your tech career begin? Were you always interested in working in technology?
My fascination with computers began in high school, my senior year, programming in BASIC on an HP 2000 mini that we time-shared into. I remember thinking this was the coolest thing—being able to tell this machine what and how to do something. And I was hooked.
I went to a two-year SUNY [State College of New York] college and majored in data processing. We did everything in batch on IBM punch cards, so you really had to love what you were doing to enjoy that. I learned IBM Assembler (BAL), Cobol, Fortran, RPG, and APL.
In the late ’70s, big companies were aggressively interviewing and hiring programmers, and I was lucky enough to be hired by Mutual of New York (MONY) to start in their programmer-training class right after graduation in June 1980.
I had just turned 20, and I had my dream job as a mainframe COBOL programmer at a big company. I thought I was set for life. Then along came the IBM PC.
When did you start reading PC MagazinePC Magazine? Can you tell us about your job at that time?
I would have to say somewhere around 1984. I had left MONY that year and went to work for Empire Blue Cross, where I was still a mainframe programmer. But that’s where I broke into the PC world. I had written an algorithm to perform database searches using like-sounding names—Soundex, I believe it was called. The big boss was very impressed and asked if I’d like the challenge of writing an application on an IBM PC to do Medicare Part B data entry offline, without being connected to the mainframe. Holy %$#@! I’d died and gone to heaven!
So I spent the next year programming this beast in the newly minted dBase III. The biggest problem with dBase at the time was it was very slow, and you had to load the entire dBase package. Your app ran through its interpreter. But I found a solution in an ad from your magazine, which I had begun practicing cover to cover that year. The solution was the dBase Compiler known as Clipper.
But I learned much more than that from PC Magazine. Tech articles and reviews helped me to no end in grounding myself firmly in the PC world. I read other magazines, especially Byte and later PC Tech Journal, but PC Magazine was always the first one I opened and digested.
In 1986, I left Blue Cross and went back to MONY to work in its newly formed Office Systems and Technologies group. There, I discovered Windows and also the newly created IBM/Microsoft OS/2. Around 1987 or 1988, I was recommending IBM PS/2 Micro Channel desktops, mostly the Model 50. They were still mostly 286-processor-based and running DOS 3.1, I believe; 1MB of RAM and a 30MB or 60MB hard drive.
The big option everyone had was the 3270 Emulation Card, which allowed multiple mainframe sessions users could bounce between. They would have the DOS session with a batch menu to allow word processing (Displaywriter or MultiMate), Lotus 1-2-3, and later, a database app, such as dBase. Micro Channel was a huge deal for an IBM shop. Also, networking began with IBM Token Ring, which in the late ’80s mostly meant connecting a small group of users to a laser printer. Then Novell NetWare came along and changed how I and the company thought of networking.
How were you chosen to star in our ad?
I was a huge proponent of OS/2…. I went to the first, newly minted PC Expo, and I was introduced around as an OS/2 influencer. I met Bill Gates and your late, great editor, Bill Machrone.
I can’t say for sure, but it must have been that meeting that spawned the influencer ad. I was not the only one; I believe there were several others throughout that year, but I think I was first…I just remember how surreal it felt, and [wondering] how in the hell did this happen to me?
You had a long career until your retirement last year. What was your favorite job and why?
I would say, without a doubt, the years I was at MONY as part of the Office Systems group. It lasted only five years, until we were absorbed into the mainframe systems operations group, but those years were the time of my life—being so young, in my twenties, and being involved in so many tech decisions. I became a Novell Engineer (CNE) before I left MONY and rode that wave throughout the ’90s. PCMag helped me a great deal, and what I said in the ad was the truth, which I stand by to this day.
What technology of the past 40 years has surprised or impressed you the most?
Software-wise, that would be Linux. I was involved somewhat with Unix before Linux came along, and I was impressed with the whole Linux-and-open-source revolution.
Hardware-wise, it would have to be the handheld computer we call the smartphone. I’ve been a handheld computer user since the Apple Newton (now there’s a war story!) and the PalmPilot. The smartphone, be it an iPhone or Android (I’ve used both) is, to me, just incredible.
What kind of technology are you most interested in now?
The smartphone for sure. I’m on team Android right now and enjoying the hell out of my new Samsung Galaxy Z Fold 4. I just can’t believe the power we carry in our hands every day and how sad it is that it’s mostly used for trivial, pointless pursuits.
Any thoughts on where tech is going in the next few decades?
Artificial intelligence and the man/machine interface. I haven’t thought about this a lot, but a breakthrough needs to happen to get to the next level, where we’re not poking at tiny keyboards and squinting at too-small screens. I’ve become a student of history, which gives us a good perspective.
Around the turn of the 20th century, humanity thought we had reached our peak. The industrial revolution had provided us with benefits beyond our wildest dreams. Science was thought to have learned all there was, and we had all the answers. Then Einstein showed up and upended the apple cart. We can only hope some very smart someone upends our applecart and gets us to the next level of technology.
The CEO of a business warfare and counterintelligence company issued a chilling warning Thursday evening about the threat posed by the Chinese Communist Party and called on Americans to change their behavior and attitude toward China’s communist regime.
“It’s not just military. I’d like to back it up all the way to hybrid warfare. Hybrid warfare is weakening your enemy by all means necessary—except for warfare, not conventional warfare,” Casey Fleming, chairman and chief executive officer of BlackOps Partners, told an audience at The Heritage Foundation on Capitol Hill. (The Daily Signal is Heritage’s multimedia news organization.)
Fleming said over 100 methods of hybrid warfare exist, citing fentanyl, a highly-lethal synthetic opioid, and the popular Chinese-owned app TikTok, which he described as a “weaponized military application in the hands of our children.”
“This is World War III. It’s 1939 all over again. You’ve got four nation-states … plus a terrorist organization, allied against the free world,” Fleming said during a panel discussion following a preview of Epoch Times’ upcoming documentary “The Final War,” which takes a deeper look into the threat of the communist regime led by Chinese President Xi Jinping since 2012.
“And it’s been going on, as it was mentioned in the [Epoch Times] documentary, it’s been going on [using] all methods except military at this point,” Fleming said.
Fleming, who also was the founding managing director of IBM’s cyber division and a managing director for global strategy at London-based profession services company Deloitte, said:
Yes, they’re building up their military significantly and very rapidly. But everybody needs to understand, hybrid warfare is going on in your classrooms. It’s going on in your living rooms.
It’s going on in your hotels. Everywhere you are. Understand, anything that we do, any business we do with China is being weaponized against us. OK? And from a cyber perspective, you name it. I mean, it’s everywhere.
Fleming called on the Heritage audience for “The Final War” to take action by standing up for the U.S. against the Chinese Communist Party:
I hope it changes your behavior tomorrow morning, and I hope it also changes your behavior to stand up for your country and get this word out to absolutely everyone.
I would also like to say, when you’re at war, there is no Left. There is no Right. There is no black person. There is no white person. We’re all in this together, OK, and we’re at war.
Peter Navarro, another panelist who was an assistant to the president during the Trump administration, said the Biden administration isn’t taking the threat of the Chinese Communist Party seriously:
The problem is that so many people within that [Biden] regime have been compromised by [Chinese Communist Party] money in some way. If you look at the feeder routes for people who wind up in positions of power in political regimes, they come from think tanks, they come from academia, they come from corporate America. That’s basically the three feeder routes.
You don’t see a lot of directors of national intelligence from the factory floors of Ohio, right? So the problem is that these people, along their way, have been fed by the [Chinese Communist Party] hand. Our universities are compromised. Our corporations are compromised. Our think tanks, in many ways, are compromised.
Sean Lin, the evening’s third panelist, served as an Army microbiologist.
“To me, the key message is that we definitely underestimate the strength of the Communist Party, and even nowadays we still [are] in our foreign policy talking [about] the Communist Party as a competitor,” Lin, who says he survived the Tiananmen Square massacre in 1989, said. “I think we still underestimate the Communist Party.”
The nearly three-hour documentary, not yet released, also highlights how China’s leaders have strategized since Mao Zedong’s communist takeover in October 1949 to become the world’s No. 1 superpower.
“Just like the documentary mentioned, this is [a plan unfolding over] 100 years,” Lin said. “Strategies have been implemented in different ways, manifested in different ways, and we just still [are] naively thinking that they want to be an open society, the Communist Party can change, maybe under different leadership.”
“Even nowadays … people believe you can take down Xi Jinping, pick another Communist Party leader, maybe have a future, become more transparent,” he said. “But I think right now we already past [that] time.”
Have an opinion about this article? To sound off, please email letters@DailySignal.com and we’ll consider publishing your edited remarks in our regular “We Hear You” feature. Remember to include the URL or headline of the article plus your name and town and/or state.
New Jersey, United States, Sept. 18, 2022 /DigitalJournal/ The Design Thinking Market research report provides all the information related to the industry. It gives the markets outlook by giving authentic data to its client which helps to make essential decisions. It gives an overview of the market which includes its definition, applications and developments, and technology. This Design Thinking market research report tracks all the exact developments and innovations in the market. It gives the data regarding the obstacles while establishing the business and guides to overcome the upcoming challenges and obstacles.
Design thinking is a complex process that helps marketers understand what their customers need and how to create innovative solutions to their problems. It is a multi-step process that requires a diverse set of talents and perspectives to come up with innovative concepts. A large number of design toolkits have been created by various vendors in the market and consist of various approaches to increase innovation efficiency and speed up this process. The design thinking process includes defining a problem, exploring the idea, visualizing solutions, integrating and feedback, and then launching the solution.
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This Design Thinking research report throws light on the major market players thriving in the market; it tracks their business strategies, financial status, and upcoming products.
Some of the Top companies Influencing this Market include:IBM Corporation, UpBOARD, Adobe Systems, Enigma, IDEO, Planbox, Intuit
Firstly, this Design Thinking research report introduces the market by providing an overview that includes definitions, applications, product launches, developments, challenges, and regions. The market is forecasted to reveal strong development by driven consumption in various markets. An analysis of the current market designs and other basic characteristics is provided in the Design Thinking report.
The region-wise coverage of the market is mentioned in the report, mainly focusing on the regions:
Segmentation Analysis of the market
The market is segmented based on the type, product, end users, raw materials, etc. the segmentation helps to deliver a precise explanation of the market
Market Segmentation: By Type
Market Segmentation: By Application
BFSI, Automotive, Electrical and Electronics, Pharmaceutical, Retail and E-commerce
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An assessment of the market attractiveness about the competition that new players and products are likely to present to older ones has been provided in the publication. The research report also mentions the innovations, new developments, marketing strategies, branding techniques, and products of the key participants in the global Design Thinking market. To present a clear vision of the market the competitive landscape has been thoroughly analyzed utilizing the value chain analysis. The opportunities and threats present in the future for the key market players have also been emphasized in the publication.
This report aims to provide:
Table of Contents
Global Design Thinking Market Research Report 2022 – 2029
Chapter 1 Design Thinking Market Overview
Chapter 2 Global Economic Impact on Industry
Chapter 3 Global Market Competition by Manufacturers
Chapter 4 Global Production, Revenue (Value) by Region
Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions
Chapter 6 Global Production, Revenue (Value), Price Trend by Type
Chapter 7 Global Market Analysis by Application
Chapter 8 Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders
Chapter 11 Market Effect Factors Analysis
Chapter 12 Global Design Thinking Market Forecast
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Oct 11, 2022 (Alliance News via COMTEX) -- The report provides a comprehensive analysis of segments in this market, covering all the major regions and countries. The major regions analyzed in the study are North America, Europe, Asia Pacific, Latin America and Middle East & Africa. The report also includes additional information about other factors such as drivers, restraints and challenges faced by this market along with an overview for each mentioned segment in the study.
The Europe Enterprise Artificial Intelligence Market would witness market growth of 30.3% CAGR during the forecast period (2022-2028).
Statistical and cogent models for the market were used to assess and forecast the market data. Additionally, market shares and important trends were taken into account when creating the study. The Market Time Line Analysis, Vendor Positioning Grid, Market Overview and Guide, Company Market Share Analysis, Company Positioning Grid, Standards of Measurement, Top to Bottom Analysis, and Vendor Share Analysis are additional data models that can use.
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The study offers in-depth regional analysis of market scenarios, by analyzing previous trends and covering future forecast. It also offers an in-depth analysis of major driving factors, segments, regions & countries and key players in the market. Moreover, the report outlines the competitive scenarios across different geographies, along with some key market strategies such as mergers and acquisitions, new product developments, R&D activities and more.
Europe Enterprise Artificial Intelligence MarketSize, Share & Industry Trends Analysis Report By Vertical, By Deployment Type (Cloud and On-premise), By Organization Size, By Technology, By Country and Growth Forecast, 2022 - 2028
The main causes propelling the demand for artificial intelligence in organizations include the growing application of AI to Strengthen customer happiness, offer better organization management, and organize data sets. To meet the rising need for AI in the industry, a number of AI service providers are innovating and producing new products & services in partnership with numerous government institutes including research & development departments.
For example, in May 2022, IBM Corporation established a strategic partnership with first artificial intelligence research institution, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), situated in Abu Dhabi, United Arab Emirates (UAE). The partnership is focused on advancing fundamental AI research as well as speeding up other types of scientific discoveries that would increase AI's capacity to assist in problem-solving in the future.
Popular themes like the industrial revolution and industrial automation are to blame for the rising demand in the European region. With advances in machine learning, the regional businesses have been recognized to invest in a range of automation technologies, including artificial intelligence, robotics, etc. The development of cognitive computing is anticipated to make it possible for regional businesses to replicate human abilities such as sensory perception, thinking, learning, and decision-making.
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The Germany market dominated the Europe Enterprise Artificial Intelligence Market by Country in 2021, and would continue to be a dominant market till 2028; thereby, achieving a market value of $4,343.3 million by 2028. The UK market is anticipated to grow at a CAGR of 29.2% during (2022 - 2028). Additionally, The France market would exhibit a CAGR of 31.3% during (2022 - 2028).
Based on Vertical, the market is segmented into IT & Telecom, BFSI, Automotive & Transportation, Retail, Media & Advertising, Healthcare & Life Sciences, and Others. Based on Deployment Type, the market is segmented into Cloud and On-premise. Based on Organization Size, the market is segmented into Large Enterprises and Small & Medium Enterprises. Based on Technology, the market is segmented into Natural Language Processing (NLP), Machine Learning, Computer Vision, Speech Recognition, and Others. Based on countries, the market is segmented into Germany, UK, France, Russia, Spain, Italy, and Rest of Europe.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Google LLC, Amazon Web Services, Inc., IBM Corporation, Apple, Inc., SAP SE, Wipro Limited., MicroStrategy, Inc., NVIDIA Corporation, Verint Systems, Inc., and IPsoft, Inc.
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Scope of the Study
Market Segments covered in the Report:
IT & Telecom
Automotive & Transportation
Media & Advertising
Healthcare & Life Sciences
By Deployment Type
By Organization Size
Small & Medium Enterprises
Natural Language Processing (NLP)
Rest of Europe
Amazon Web Services, Inc.
Verint Systems, Inc.
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