Today's artificial intelligence (AI) technologies are usually run using machine learning algorithms. These operate on what's called a neural network — systems designed to mimic the human brain inner workings — as part of what is called deep learning. Currently, most AI advances are largely due to deep learning, with developments like AlphaGo, the Go-playing AI created by Google's DeepMind.
Now, IBM has announced that they have developed an AI that makes the entire machine learning process faster. Instead of running complex deep learning models on just a single server, the team, led by IBM Research's director of systems acceleration and memory Hillery Hunter, managed to efficiently scale up distributed deep learning (DDL) using multiple servers.
"The idea is to change the rate of how fast you can train a deep learning model and really boost that productivity," Hunter told Fortune. Previously, it was difficult to implement DDL setups because of the complexity needed to keep the processors in-sync. The IBM Research team managed to use 64 of its Power 8 servers to facilitate data processing. Each processor was linked using Nvidia graphical processors and a fast NVLink interconnection, resulting in what Hillery's team calls PowerAI DDL.
Instead of taking days for a deep learning network to process models, it could now take only hours. "Our objective is to reduce the wait-time associated with deep learning training from days or hours to minutes or seconds, and enable improved accuracy of these AI models," Hunter wrote in an IBM Research blog.
In their study published online, the team claimed that they managed a 95 percent scaling efficiency across 256 processors when they ran the setup using a deep learning framework developed at the University of California Berkeley. They also recorded a 33.8 percent image recognition accuracy rate, processing 7.5 million images in a little over seven hours, beating Microsoft's record of 29.8 percent in 10 days.
Some, however, are skeptical of the achievement. Patrick Moorhead, president and founder a Texas-based tech research firm told Fortune that 95 percent seemed to0 good to be true. Still, IBM's achievement could potentially boost the capabilities of deep learning networks. It could lead to improvements in how AI helps in medical research and in autonomous systems, cutting down the time necessary to make big progress.
It’s not a robot. It’s the employee of the future. (Illustration: Andrés Moncayo)
In 2009, Pep Boys, the nationwide auto parts and service chain realized that their traditional ways of educating their employees about theft—through posters, classes, and meetings—weren’t really working. They turned to a new Canadian-based startup called Axonify to try a different approach, where the information was stripped down to the most critical concepts and presented more like mobile games: quick sessions that employees could complete on their phone in just three minutes each day. Using the system was voluntary, with the incentive of earning points that could be redeemed for rewards.
The program didn’t take long to prove its worth. Unlike many corporate learning systems, not only did employees use the system, but doing so generated measurable business results: Pep Boys saw their losses due to theft at their more than 700 stores drop by $20 million in the first year alone, because their employees were better able to identify suspicious behavior and report it properly. Before the experiment, “they took for granted that employees knew what to do,” says Axonify CEO Carol Leaman, but it turned out that they needed to actually learn theft prevention tactics, not just be exposed to it.
The human resources industry is in the midst of a huge shift in how it thinks about employee training and learning. “A lot of other areas of business have already been transformed through technology, but HR, as is often the case, hasn’t had the same level of investment until rather recently,” says Jon Ingham, a UK-based consultant in human capital management. The HR software market is now estimated at $15 billion, but not all of that money is being put to good use. According to analyst Josh Bersin, despite the fact that learning management systems are the fastest growing segment (currently worth about $2.5 billion), up to 30 percent of the corporate training material that companies develop is wasted.
The very idea that training should be measured by what employees actually learn is a conceptual breakthrough in and of itself. In the 1990s, traditional classroom training started to supply way to “learning management systems,” which helped companies better scale their training efforts, because instruction could be centralized and distributed on-demand via their corporate intranet. But the data and reports they generated were primitive. “At that time, it was very much about who attended the courses,” says Jonathan Ferrar, vice president of IBM’s Smarter Workforce, “but that’s of almost no value. What companies really want to know is whether employees actually learn and retain the information, and whether it’s the right information for improving business performance.”
Advances in big data analysis and machine learning now allow IBM to isolate variables and discover which are responsible for significant learning insights. “Five years ago, that type of analysis would take statisticians and data scientists days or weeks,” says Ferrar, “but now it can be done in minutes or hours.” He notes that when companies have an accurate assessment of employee knowledge, they can actually save money. “Rather than wasting employee time by making everyone sit through an hour-long compliance training each year, for example, companies should first find out who actually needs the training, and who already knows the regulatory standards.”
In Axonify’s platform, assessment and training are directly tied together. Because many employees use Axonify regularly, the platform is able to constantly track employee knowledge and intelligently provide the information needed to close an employee’s individual knowledge gap, says Leaman. The app also leverages learning research to optimize retention by repeating the questions in specific time intervals. Even after an employee “graduates” out of a specific topic, the questions will still be revisited about seven months later to help lock in the knowledge.
IBM uses behavior data a bit differently, to deliver useful training materials to employees when they actually need it. For example, when a new IBM employee schedules their first meeting with other employees, the assistant detects that it’s their first time, and proactively presents material about how to conduct a meeting. “We’re closing the gap between new and experienced employees, and accelerating that transition,” says Kramer Reeves, IBM’s director of messaging and collaboration solutions..
Traditional Classroom Training
How did it work? Exactly how you’d expect it to work. In person lectures gathered employees and trained them collectively in organized sessions.
What did it measure? Little more than attendance and, if there were tests and quizzes, individual performance scores.
Learning Management Systems
How did it work? It brought the classroom experience to the computer screen and removed the need for in-person lectures or sessions. Training could now be done individually at the employee’s convenience.
What did it measure? LMS’s were largely limited to measuring completion of the training and, if there were tests and quizzes, individual performance scores.
The Big Data-Driven LMS
How does it work? With new tools in big data analysis and machine learning, you can identify insights of what works and what doesn’t in your training tools in minutes—as opposed to days in the past.
What does it measure? Big data can definitively show how well your training works—making the process more efficient and cutting down on unnecessary training.
The Smart LMS
How does it work? Training’s been unbundled and different tools teaching different skills can be deployed a la carte when relevant challenges are encountered.
What does it measure? The Smart LMS can measure how often different skills in the position are needed and how necessary training is for the various skills.
The Social LMS
How does it work? The social web has broken down walls that once resulted in employees being trained in a vacuum. Instead of having a single system that teaches all employees the same things, new employees can learn from experienced ones.
What does it measure? By bringing together the training needs of new employees with the experience of more tenured ones, employers can better close the knowledge gap between them.
Why does all this matter?
U.S. organizations spent $171.5 billion on employee training and development in 2010 and $156.2 billion in 2012.
But to really get insight about what employees know and how they’re learning, analytics systems will need to take into account more than just HR-provided training material. “The things that happen in a learning management system are less than ten percent of the activities that real people pursue when they want to learn something,” says Tim Martin, a co-founder of Rustici Software. “If you want to learn something, you don’t go to an LMS, whether you have access to it or not—you usually go to Google or a co-worker.”
Martin is one of the creators of the Tin Can API, a new standard for communicating and storing information about employee learning events. Tin Can is the modern successor to SCORM, a specification that was originally created to standardize content across different learning management systems. The only things that SCORM could measure and track were those where a single user was logged into a learning management system, taking a prescribed piece of training in an active browser session. Tin Can, on the other hand allows companies and employees to record more common learning events: attending a session at a conference, say, or researching and writing a company blog post. “Companies are starting to recognize how employees actually learn and allowing them to do it the way they wish to, rather than forcing them into a draconian system,” Martin says.
Reeves says that this type of outside integration is part of a larger trend in IT departments. More and more CEOs are demanding technology solutions that support external collaboration, according to IBM surveys. Across industries, companies are shifting from controlled, closed environments to more open environments. It’s no longer feasible to expect a single program or tool to do everything—instead, employees expect multiple applications to work well together in a useful way.
One example of useful linking is the way IBM has integrated social collaboration tools into their talent management and learning systems. Social interaction has long been missing from virtual classroom instruction, and after all, learning is “very much a social activity,” says Jacques Pavlenyi, IBM’s program manager for social collaboration software marketing. IBM has found that employees learn and retain more when they’re working socially.
As job-related learning becomes more user-friendly and comprehensive, it also empowers employees to Strengthen their own performance. Leaman says that in surveys of why employees voluntarily use Axonify, she was surprised to see that the most common reason wasn’t the rewards offered, but “because it helps me do my job better.” When people have knowledge, she says, they feel more empowered, more confident in taking action, and “are actually much better employees.”
Ten years ago, says Ingham, HR technology was mostly meant to be used by the HR department, whereas now companies are more focused on employees themselves as the primary users. In the future, Ingham would like companies to use technology not to control employees, but to enable and liberate them to increase their own performance. “The opportunity is not to use analytics to control but to supply employees meaningful data about the way they’re operating within an organization so that they themselves can do things to Strengthen their working lives and their performance,” he says.
New Jersey, United States-Machine Learning as a Service (MLaaS) Market 2022 – 2030, Size, Share, and Trends Analysis Research Report Segmented with Type, Component, Application, Region, and Forecast
The size of the machine learning as a service market worldwide was valued at $13.95 billion in 2020 and is projected to reach $302.67 billion by 2030, developing at a CAGR of 36.3% from 2021 to 2030. Machine learning is a course of data analysis that involves statistical data analysis performed to determine wanted prescient results without the implementation of express programming. It is intended to incorporate functionalities of artificial insight (AI) and mental registering including a progression of algorithms and is utilized to understand the relationship between datasets to obtain an ideal result. Machine learning as a service (MLaaS) incorporates a scope of administrations that deal with machine learning instruments through distributed computing services.
Major Machine learning as a Service Market Growth drivers incorporates an increased market for distributed computing and development associated with artificial insight and mental processing. Important impacting factors of the machine learning as a service remember development for demand for cloud-based arrangements, remembering development for demand for distributed computing, ascend in the adoption of analytical arrangements, development of artificial knowledge and mental registering market, increased application areas, and dearth of trained professionals.
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Increasing need to understand the client behavior, developing adoption of machine learning as a service (MLaaS) arrangements by small and medium-scale organizations and flood in the concentration on advancements in data science innovation are the major factors attributable to the development of the machine learning as a service (MLaaS) market.
MLaaS is considered as a sub-class of circulated registering administrations. MLaaS is a variety of administrations that offers an extensive variety of machine learning devices and parts to undertake operations with greater proficiency and viability. Increased demand for the web of things innovation will arise as the major market development driving factor. Developing advancements in artificial knowledge innovation will additionally aggravate the development of the market.
The machine learning as a service market is portioned into By Application, By Organization Size, By Component, and By End-Use Industry. Contingent upon part, the ML as a Service market is separated into software and services. Based on the organization size, it is separated into large endeavors and small and medium undertakings. On the basis of end-client industry, it is separated into aerospace and guard, BFSI, public area, retail, healthcare, IT and telecom, energy and utilities, manufacturing, and others. On the basis of application, it is separated into marketing and advertising, fraud discovery and chance management, prescient analytics, augmented and virtual reality, natural language handling, PC vision, security and surveillance, and others.
Based on components, the service segment dominated machine learning as a service market share and is supposed to maintain its dominance in the impending years. This is attributed to factors, for example, an increase in application areas and development associated with end-use enterprises among creating economies is supposed to drive the market development for machine learning services. Industry players are engaged in the implementation of technologically advanced answers for increased adoption of machine learning services. Utilization of machine learning services in the healthcare business for recognition of cancer as well as to check ECG and MRI increases the market in the healthcare area.
North America is the fast-developing district in the global machine learning as a service market, regarding technological advancements and adoption. It has an exceptional infrastructure and the ability to afford machine learning as a service arrangement. Besides, ascend in interests in the guard area, along with technological advancements in the telecommunication business, is supposed to drive the market development during the forecast time frame. Unofficial laws regarding data security are supposed to keep on being areas of strength for a for the machine learning services market. Services, for example, security information and cloud application are supposed to drive the market. In addition, solid presence of industry leaders like Google, IBM, Microsoft, and Amazon Web Services and enhanced item contributions have additionally prompted ascend in demand for machine learning around here. Moreover, development associated with artificial knowledge and mental registering is supposed to create lucrative open doors for the market players to leverage varied industry applications, for example, prescient analytics, natural language handling, PC vision, fraud recognition and management.
The key players of the industry are Google, IBM, Microsoft and Amazon among many other companies in the industry.
The following are some of the reasons why you should take a Machine Learning as a Service (MLaaS) market report:
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Database professionals are in high demand. If you already work as one, you probably know this. And if you are looking to become a database administrator, that high demand and the commensurate salary may be what is motivating you to make this career move.
How can you advance your career as a database administrator? By taking the courses on this list.
If you want to learn more about database administration to expand your knowledge and move up the ladder in this field, these courses can help you achieve that goal.
Udemy’s Oracle DBA 11g/12c – Database Administration for Junior DBA course can help you get a high-paying position as an Oracle Database Administrator.
Best of all, it can do it in just six weeks.
This database administrator course is a Udemy bestseller that is offered in eight languages. Over 29,000 students have taken it, giving it a 4.3-star rating. Once you complete it and become an Oracle DBA, you will be able to:
To take the intermediate-level course that includes 11 hours of on-demand video spanning 129 lectures, you should have basic knowledge of UNIX/LINUX commands and SQL.
The 70-462: SQL Server Database Administration (DBA) course from Udemy was initially designed to help beginner students ace the Microsoft 70-462 exam. Although that exam has been officially withdrawn, you can still use this course to gain some practical experience with database administration in SQL Server.
Many employers seek SQL Server experience since it is one of the top database tools. Take the 70-462: SQL Server Database Administration (DBA) course, and you can gain valuable knowledge on the course and supply your resume a nice boost.
Some of the skills you will learn in the 70-462 course include:
DBA knowledge is not needed to take the 10-hour course that spans 100 lectures, and you will not need to have SQL Server already installed on your computer. In terms of popularity, this is a Udemy bestseller with a 4.6-star rating and over 20,000 students.
Nearly 10,000 students have taken the MySQL Database Administration: Beginner SQL Database Design course on Udemy, making it a bestseller on the platform with a 4.6-star rating.
The course features 71 lectures that total seven hours in length and was created for those looking to gain practical, real-world business intelligence and analytics skills to eventually create and maintain databases.
What can you learn from taking the Beginner SQL Database Design course? Skills such as:
The requirements for taking this course are minimal. It can help to have a basic understanding of database fundamentals, and you will need to install MySQL Workbench and Community Server on your Mac or PC.
If you want to immerse yourself into the world of database administration and get a ton of bang for your buck, TechRepublic Academy’s Database Administration Super Bundle may be right up your alley.
It gives you nine courses and over 400 lessons equaling over 86 hours that can put you on the fast track to building databases and analyzing data like a pro. A sampling of the courses offered in this bundle include:
Here is another bundle for database administrators from TechRepublic Academy. With the Ultimate SQL Bootcamp, you get nine courses and 548 lessons to help you learn how to:
The Complete Oracle Master Class Bundle from TechRepublic Academy features 181 hours of content and 17 courses to help you build a six-figure career. This intermediate course includes certification and will supply you hands-on and practical training with Oracle database systems.
Some of the skills you will learn include:
Coursera’s Learn SQL Basics for Data Science Specialization course has nearly 7,000 reviews, giving it a 4.5-star rating. Offered by UC Davis, this specialization is geared towards beginners who lack coding experience that want to become fluent in SQL queries.
The specialization takes four months to complete at a five-hour weekly pace, and it is broken down into four courses:
Skills you can gain include:
Once finished, you will be able to analyze and explore data with SQL, write queries, conduct feature engineering, use SQL with unstructured data sets, and more.
IBM offers the Relational Database Administration (DBA) course on Coursera with a 4.5-star rating. Complete the beginner course that takes approximately 19 hours to finish, and it can count towards your learning in the IBM Data Warehouse Engineer Professional Certificate and IBM Data Engineering Professional Certificate programs.
Some of the skills you will learn in this DBA course include:
Offered by Oracle, the Autonomous Database Administration course from Coursera has a 4.5-star rating and takes 13 hours to complete. It is meant to help DBAs deploy and administer Autonomous databases. Finish it, and you will prepare yourself for the Oracle Autonomous Database Cloud Certification.
Some of the skills and knowledge you can learn from this course include:
Looking for more database administration and database programming courses? Check out our tutorial: Best Online Courses to Learn MySQL.
Interested, he talked to a counselor to learn more about P-TECH, an early college program where he could earn an associate’s degree along with his high school diploma. Liking the sound of the program, he enrolled in the inaugural P-TECH class as a freshman at Longmont’s Skyline High School.
“I really loved working on computers, even before P-TECH,” he said. “I was a hobbyist. P-TECH gave me a pathway.”
IBM hired him as a cybersecurity analyst once he completed the apprenticeship.
“P-TECH has given me a great advantage,” he said. “Without it, I would have been questioning whether to go into college. Having a college degree at 18 is great to put on a resume.”
Litow’s idea was to get more underrepresented young people into tech careers by giving them a direct path to college while in high school — and in turn create a pipeline of employees with the job skills businesses were starting to value over four-year college degrees.
The program, which includes mentors and internships provided by business partners, gives high school students up to six years to earn an associate's degree at no cost.
In Colorado, St. Vrain Valley was among the first school districts chosen by the state to offer a P-TECH program after the Legislature passed a bill to provide funding — and the school district has embraced the program.
Colorado’s first P-TECH programs started in the fall of 2016 at three high schools, including Skyline High. Over the last six years, 17 more Colorado high schools have adopted P-TECH, for at total of 20. Three of those are in St. Vrain Valley, with a fourth planned to open in the fall of 2023 at Longmont High School.
Each St. Vrain Valley high school offers a different focus supported by different industry partners.
Skyline partners with IBM, with students earning an associate’s degree in Computer Information Systems from Front Range. Along with being the first, Skyline’s program is the largest, enrolling up to 55 new freshmen each year.
Programs at the other schools are capped at 35 students per grade.
Frederick High’s program, which started in the fall of 2019, has a bioscience focus, partners with Aims Community College and works with industry partners Agilent Technologies, Tolmar, KBI Biopharma, AGC Biologics and Corden Pharma.
Silver Creek High’s program started a year ago with a cybersecurity focus. The Longmont school partners with Front Range and works with industry partners Seagate, Cisco, PEAK Resources and Comcast.
The new program coming to Longmont High will focus on business.
District leaders point to Skyline High’s graduation statistics to illustrate the program’s success. At Skyline, 100 percent of students in the first three P-TECH graduating classes earned a high school diploma in four years.
For the 2020 Skyline P-TECH graduates, 24 of the 33, or about 70 percent, also earned associate’s degrees. For the 2021 graduating class, 30 of the 47 have associate’s degrees — with one year left for those students to complete the college requirements.
For the most accurate 2022 graduates, who have two years left to complete the college requirements, 19 of 59 have associate’s degrees and another six are on track to earn their degrees by the end of the summer.
Louise March, Skyline High’s P-TECH counselor, keeps in touch with the graduates, saying 27 are working part time or full time at IBM. About a third are continuing their education at a four year college. Of the 19 who graduated in 2022 with an associate’s degree, 17 are enrolling at a four year college, she said.
Two of those 2022 graduates are Anahi Sarmiento, who is headed to the University of Colorado Boulder’s Leeds School of Business, and Jose Ivarra, who will study computer science at Colorado State University.
“I’m the oldest out of three siblings,” Ivarra said. “When you hear that someone wants to supply you free college in high school, you take it. I jumped at the opportunity.”
Sarmiento added that her parents, who are immigrants, are already working two jobs and don’t have extra money for college costs.
“P-TECH is pushing me forward,” she said. “I know my parents want me to have a better life, but I want them to have a better life, too. Going into high school, I kept that mentality that I would push myself to my full potential. It kept me motivated.”
While the program requires hard work, the two graduates said, they still enjoyed high school and had outside interests. Ivarra was a varsity football player who was named player of the year. Sarmiento took advantage of multiple opportunities, from helping elementary students learn robotics to working at the district’s Innovation Center.
Ivarra said he likes that P-TECH has the same high expectations for all students, no matter their backgrounds, and gives them support in any areas where they need help. Spanish is his first language and, while math came naturally, language arts was more challenging.
“It was tough for me to see all these classmates use all these big words, and I didn’t know them,” he said. “I just felt less. When I went into P-TECH, the teachers focus on you so much, checking on every single student.”
They said it’s OK to struggle or even fail. Ivarra said he failed a tough class during the pandemic, but was able to retake it and passed. Both credited March, their counselor, with providing unending support as they navigated high school and college classes.
“She’s always there for you,” Sarmiento said. “It’s hard to be on top of everything. You have someone to go to.”
Students also supported each other.
“You build bonds,” Ivarra said. “You’re all trying to figure out these classes. You grow together. It’s a bunch of people who want to succeed. The people that surround you in P-TECH, they push you to be better.”
P-TECH has no entrance requirements or prerequisite classes. You don’t need to be a top student, have taken advanced math or have a background in technology.
With students starting the rigorous program with a wide range of skills, teachers and counselors said, they quickly figured out the program needed stronger support systems.
March said freshmen in the first P-TECH class struggled that first semester, prompting the creation of a guided study class. The every other day, hour-and-a-half class includes both study time and time to learn workplace skills, including writing a resume and interviewing. Teachers also offer tutoring twice a week after school.
“The guided study has become crucial to the success of the program,” March said.
Another way P-TECH provides extra support is through summer orientation programs for incoming freshmen.
At Skyline, ninth graders take a three-week bridge class — worth half a credit — that includes learning good study habits. They also meet IBM mentors and take a field trip to Front Range Community College.
“They get their college ID before they get their high school ID,” March said.
During a session in June, 15 IBM mentors helped the students program a Sphero robot to travel along different track configurations. Kathleen Schuster, who has volunteered as an IBM mentor since the P-TECH program started here, said she wants to “return some of the favors I got when I was younger.”
“Even this play stuff with the Spheros, it’s teaching them teamwork and a little computing,” she said. “Hopefully, through P-TECH, they will learn what it takes to work in a tech job.”
Incoming Skyline freshman Blake Baker said he found a passion for programming at Trail Ridge Middle and saw P-TECH as a way to capitalize on that passion.
“I really love that they supply you options and a path,” he said.
Trail Ridge classmate Itzel Pereyra, another programming enthusiast, heard about P-TECH from her older brother.
“It’s really good for my future,” she said. “It’s an exciting moment, starting the program. It will just help you with everything.”
While some of the incoming ninth graders shared dreams of technology careers, others see P-TECH as a good foundation to pursue other dreams.
Skyline incoming ninth grader Marisol Sanchez wants to become a traveling nurse, demonstrating technology and new skills to other nurses. She added that the summer orientation sessions are a good introduction, helping calm the nerves that accompany combining high school and college.
“There’s a lot of team building,” she said. “It’s getting us all stronger together as a group and introducing everyone.”
Silver Creek’s June camp for incoming ninth graders included field trips to visit Cisco, Seagate, PEAK Resources, Comcast and Front Range Community College.
During the Front Range Community College field trip, the students heard from Front Range staff members before going on a scavenger hunt. Groups took photos to prove they completed tasks, snapping pictures of ceramic pieces near the art rooms, the most expensive tech product for sale in the bookstore and administrative offices across the street from the main building.
Emma Horton, an incoming freshman, took a cybersecurity class as a Flagstaff Academy eighth grader that hooked her on the idea of technology as a career.
“I’m really excited about the experience I will be getting in P-TECH,’ she said. “I’ve never been super motivated in school, but with something I’m really interested in, it becomes easier.”
Deb Craven, dean of instruction at Front Range’s Boulder County campus, promised the Silver Creek students that the college would support them. She also gave them some advice.
“You need to advocate and ask for help,” she said. “These two things are going to help you the most. Be present, be engaged, work together and lean on each other.”
Craven, who oversees Front Range’s P-TECH program partnership, said Front Range leaders toured the original P-TECH program in New York along with St. Vrain and IBM leaders in preparation for bringing P-TECH here.
“Having IBM as a partner as we started the program was really helpful,” she said.
When the program began, she said, freshmen took a more advanced technology class as their first college class. Now, she said, they start with a more fundamental class in the spring of their freshman year, learning how to build a computer.
“These guys have a chance to grow into the high school environment before we stick them in a college class,” she said.
Summer opportunities aren’t just for P-TECH’s freshmen. Along with summer internships, the schools and community colleges offer summer classes.
Silver Creek incoming 10th graders, for example, could take a personal financial literacy class at Silver Creek in the mornings and an introduction to cybersecurity class at the Innovation Center in the afternoons in June.
Over at Skyline, incoming 10th graders in P-TECH are getting paid to teach STEM lessons to elementary students while earning high school credit. Students in the fifth or sixth year of the program also had the option of taking computer science and algebra classes at Front Range.
And at Frederick, incoming juniors are taking an introduction to manufacturing class at the district's Career Elevation and Technology Center this month in preparation for an advanced manufacturing class they’re taking in the fall.
“This will supply them a head start for the fall,” said instructor Chester Clark.
Incoming Frederick junior Destini Johnson said she’s not sure what she wants to do after high school, but believes the opportunities offered by P-TECH will prepare her for the future.
“I wanted to try something challenging, and getting a head start on college can only help,” she said. “It’s really incredible that I’m already halfway done with an associate’s degree and high school.”
IBM P-TECH program manager Tracy Knick, who has worked with the Skyline High program for three years, said it takes a strong commitment from all the partners — the school district, IBM and Front Range — to make the program work.
“It’s not an easy model,” she said. “When you say there are no entrance requirements, we all have to be OK with that and support the students to be successful.”
IBM hosted 60 St. Vrain interns this summer, while two Skyline students work as IBM “co-ops” — a national program — to assist with the P-TECH program.
The company hosts two to four formal events for the students each year to work on professional and technical skills, while IBM mentors provide tutoring in algebra. During the pandemic, IBM also paid for subscriptions to tutor.com so students could get immediate help while taking online classes.
“We want to get them truly workforce ready,” Knick said. “They’re not IBM-only skills we’re teaching. Even though they choose a pathway, they can really do anything.”
As the program continues to expand in the district, she said, her wish is for more businesses to recognize the value of P-TECH.
“These students have had intensive training on professional skills,” she said. “They have taken college classes enhanced with the same digital credentials that an IBM employee can learn. There should be a waiting list of employers for these really talented and skilled young professionals.”
©2022 the Daily Camera (Boulder, Colo.). Distributed by Tribune Content Agency, LLC.
Over the past decade, artificial intelligence (AI) has emerged as an engine of discovery by helping to unlock information from large repositories of previously inaccessible data. The cloud has expanded computer capacity exponentially by creating a global network of remote and distributed computing resources. And quantum computing has arrived on the scene as a game changer in processing power by harnessing quantum simulation to overcome the scaling and complexity limits of classical computing.
In parallel to these advances in computing, in which IBM is a world leader, the healthcare and life sciences have undergone their own information revolution. There has been an explosion in genomic, proteomic, metabolomic and a plethora of other foundational scientific data, as well as in diagnostic, treatment, outcome and other related clinical data. Paradoxically, however, this unprecedented increase in information volume has resulted in reduced accessibility and a diminished ability to use the knowledge embedded in that information. This reduction is caused by siloing of the data, limitations in existing computing capacity, and processing challenges associated with trying to model the inherent complexity of living systems.
IBM Research is now working on designing and implementing computational architectures that can convert the ever-increasing volume of healthcare and life-sciences data into information that can be used by scientists and industry experts the world over. Through an AI approach powered by high-performance computing (HPC)—a synergy of quantum and classical computing—and implemented in a hybrid cloud that takes advantage of both private and public environments, IBM is poised to lead the way in knowledge integration, AI-enriched simulation, and generative modeling in the healthcare and life sciences. Quantum computing, a rapidly developing technology, offers opportunities to explore and potentially address life-science challenges in entirely new ways.
“The convergence of advances in computation taking place to meet the growing challenges of an ever-shifting world can also be harnessed to help accelerate the rate of discovery in the healthcare and life sciences in unprecedented ways,” said Ajay Royyuru, IBM fellow and CSO for healthcare and life sciences at IBM Research. “At IBM, we are at the forefront of applying these new capabilities for advancing knowledge and solving complex problems to address the most pressing global health challenges.”
Innovation in the healthcare and life sciences, while overall a linear process leading from identifying drug targets to therapies and outcomes, relies on a complex network of parallel layers of information and feedback loops, each bringing its own challenges (Fig. 1). Success with target identification and validation is highly dependent on factors such as optimized genotype–phenotype linking to enhance target identification, improved predictions of protein structure and function to sharpen target characterization, and refined drug design algorithms for identifying new molecular entities (NMEs). New insights into the nature of disease are further recalibrating the notions of disease staging and of therapeutic endpoints, and this creates new opportunities for improved clinical-trial design, patient selection and monitoring of disease progress that will result in more targeted and effective therapies.
Powering these advances are several core computing technologies that include AI, quantum computing, classical computing, HPC, and the hybrid cloud. Different combinations of these core technologies provide the foundation for deep knowledge integration, multimodal data fusion, AI-enriched simulations and generative modeling. These efforts are already resulting in rapid advances in the understanding of disease that are beginning to translate into the development of better biomarkers and new therapeutics (Fig. 2).
“Our goal is to maximize what can be achieved with advanced AI, simulation and modeling, powered by a combination of classical and quantum computing on the hybrid cloud,” said Royyuru. “We anticipate that by combining these technologies we will be able to accelerate the pace of discovery in the healthcare and life sciences by up to ten times and yield more successful therapeutics and biomarkers.”
Developing new drugs hinges on both the identification of new disease targets and the development of NMEs to modulate those targets. Developing NMEs has typically been a one-sided process in which the in silico or in vitro activities of large arrays of ligands would be tested against one target at a time, limiting the number of novel targets explored and resulting in ‘crowding’ of clinical programs around a fraction of validated targets. accurate developments in proteochemometric modeling—machine learning-driven methods to evaluate de novo protein interactions in silico—promise to turn the tide by enabling the simultaneous evaluation of arrays of both ligands and targets, and exponentially reducing the time required to identify potential NMEs.
Proteochemometric modeling relies on the application of deep machine learning tools to determine the combined effect of target and ligand parameter changes on the target–ligand interaction. This bimodal approach is especially powerful for large classes of targets in which active-site similarities and lack of activity data for some of the proteins make the conventional discovery process extremely challenging.
Protein kinases are ubiquitous components of many cellular processes, and their modulation using inhibitors has greatly expanded the toolbox of treatment options for cancer, as well as neurodegenerative and viral diseases. Historically, however, only a small fraction of the kinome has been investigated for its therapeutic potential owing to biological and structural challenges.
Using deep machine learning algorithms, IBM researchers have developed a generative modeling approach to access large target–ligand interaction datasets and leverage the information to simultaneously predict activities for novel kinase–ligand combinations1. Importantly, their approach allowed the researchers to determine that reducing the kinase representation from the full protein sequence to just the active-site residues was sufficient to reliably drive their algorithm, introducing an additional time-saving, data-use optimization step.
Machine learning methods capable of handling multimodal datasets and of optimizing information use provide the tools for substantially accelerating NME discovery and harnessing the therapeutic potential of large and sometimes only minimally explored molecular target spaces.
Electronic health records (EHRs) and insurance claims contain a treasure trove of real-world data about the healthcare history, including medications, of millions of individuals. Such longitudinal datasets hold potential for identifying drugs that could be safely repurposed to treat certain progressive diseases not easily explored with conventional clinical-trial designs because of their long time horizons.
Turning observational medical databases into drug-repurposing engines requires the use of several enabling technologies, including machine learning-driven data extraction from unstructured sources and sophisticated causal inference modeling frameworks.
Parkinson’s disease (PD) is one of the most common neurodegenerative disorders in the world, affecting 1% of the population above 60 years of age. Within ten years of disease onset, an estimated 30–80% of PD patients develop dementia, a debilitating comorbidity that has made developing disease-modifying treatments to slow or stop its progression a high priority.
IBM researchers have now developed an AI-driven, causal inference framework designed to emulate phase 2 clinical trials to identify candidate drugs for repurposing, using real-world data from two PD patient cohorts totaling more than 195,000 individuals2. Extracting relevant data from EHRs and claims data, and using dementia onset as a proxy for evaluating PD progression, the team identified two drugs that significantly delayed progression: rasagiline, a drug already in use to treat motor symptoms in PD, and zolpidem, a known psycholeptic used to treat insomnia. Applying advanced causal inference algorithms, the IBM team was able to show that the drugs exert their effects through distinct mechanisms.
Using observational healthcare data to emulate otherwise costly, large and lengthy clinical trials to identify repurposing candidates highlights the potential for applying AI-based approaches to accelerate potential drug leads into prospective registration trials, especially in the context of late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.
One of the main bottlenecks in drug discovery is the high failure rate of clinical trials. Among the leading causes for this are shortcomings in identifying relevant patient populations and therapeutic endpoints owing to a fragmented understanding of disease progression.
Using unbiased machine-learning approaches to model large clinical datasets can advance the understanding of disease onset and progression, and help identify biomarkers for enhanced disease monitoring, prognosis, and trial enrichment that could lead to higher rates of trial success.
Huntington’s disease (HD) is an inherited neurodegenerative disease that results in severe motor, cognitive and psychiatric disorders and occurs in about 3 per 100,000 inhabitants worldwide. HD is a fatal condition, and no disease-modifying treatments have been developed to date.
An IBM team has now used a machine-learning approach to build a continuous dynamic probabilistic disease-progression model of HD from data aggregated from multiple disease registries3. Based on longitudinal motor, cognitive and functional measures, the researchers were able to identify nine disease states of clinical relevance, including some in the early stages of HD. Retrospective validation of the results with data from past and ongoing clinical studies showed the ability of the new disease-progression model of HD to provide clinically meaningful insights that are likely to markedly Strengthen patient stratification and endpoint definition.
Model-based determination of disease stages and relevant clinical and digital biomarkers that lead to better monitoring of disease progression in individual participants is key to optimizing trial design and boosting trial efficiency and success rates.
IBM has established its mission to advance the pace of discovery in healthcare and life sciences through the application of a versatile and configurable collection of accelerator and foundation technologies supported by a backbone of core technologies (Fig. 1). It recognizes that a successful campaign to accelerate discovery for therapeutics and biomarkers to address well-known pain points in the development pipeline requires external, domain-specific partners to co-develop, practice, and scale the concept of technology-based acceleration. The company has already established long-term commitments with strategic collaborators worldwide, including the recently launched joint Cleveland Clinic–IBM Discovery Accelerator, which will house the first private-sector, on-premises IBM Quantum System One in the United States. The program is designed to actively engage with universities, government, industry, startups and other relevant organizations, cultivating, supporting and empowering this community with open-source tools, datasets, technologies and educational resources to help break through long-standing bottlenecks in scientific discovery. IBM is engaging with biopharmaceutical enterprises that share this vision of accelerated discovery.
“Through partnerships with leaders in healthcare and life sciences worldwide, IBM intends to boost the potential of its next-generation technologies to make scientific discovery faster, and the scope of the discoveries larger than ever,” said Royyuru. “We ultimately see accelerated discovery as the core of our contribution to supercharging the scientific method.”
Deep Learning in Security Market Overview, Analysis, Outlook and Forecast to 2027
This press release was orginally distributed by SBWire
New Jersey, NJ — (SBWIRE) — 07/30/2022 — The Deep Learning in Security Market study describes how the technology industry is evolving and how major and emerging players in the industry are responding to long term opportunities and short-term challenges they face. One major attraction about Deep Learning in Security Industry is its growth rate. Many major technology players – including NVIDIA (US), Intel (US), Xilinx (US), Samsung Electronics (South Korea), Micron Technology (US), Qualcomm (US), IBM (US), Google (US), Microsoft (US), AWS (US), Graphcore (UK), Mythic (US), Adapteva (US) & Koniku (US) etc have been looking into Deep Learning in Security as a way to increase their market share and reach towards consumers.
Industries and key technological segments are evolving; navigate these changes with latest insights released on Deep Learning in Security Market Study
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Major Highlights of Deep Learning in Security Market Report
1) Why this market research study would be beneficial?
– The study guides Deep Learning in Security companies with strategic planning to ensure they realize and drive business value from their plans for growth strategy.
2) How scope of study is defined?
– The Deep Learning in Security market is composed of different product/ service offering type, each with its own business models and technology. They include:
Type: Hardware, Software & Service;
Application: Identity and Access Management, Risk and Compliance Management, Encryption, Data Loss Prevention, Unified Threat Management, Antivirus/Antimalware, Intrusion Detection/Prevention Systems & Others (Firewall, Distributed Denial-of-Service (DDoS), Disaster Recovery);
**Further breakdown / Market segmentation can be provided; subject to availability and feasibility of data.
3) Why Deep Learning in Security Market would define new growth cycle ?
– Analysis says that Deep Learning in Security Companies that have continues to invest in new products and services including via acquisitions have seen sustainable growth, whereas one with slower R&D investment growth have become stagnant. Technology companies with annual R&D growth over 20% have outperformed their peer group in revenue growth.
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Research shows that Deep Learning in Security companies have increased R&D spend and accelerated merger & acquisitions. The industry has one of the fastest innovation cycles studied across industry/applications such as Identity and Access Management, Risk and Compliance Management, Encryption, Data Loss Prevention, Unified Threat Management, Antivirus/Antimalware, Intrusion Detection/Prevention Systems & Others (Firewall, Distributed Denial-of-Service (DDoS), Disaster Recovery). To realize value they intend, companies like NVIDIA (US), Intel (US), Xilinx (US), Samsung Electronics (South Korea), Micron Technology (US), Qualcomm (US), IBM (US), Google (US), Microsoft (US), AWS (US), Graphcore (UK), Mythic (US), Adapteva (US) & Koniku (US) etc need to continuously evaluate their governance, risks and control, infrastructure, and talent to aligned planned growth strategies with their operating business models.
To comprehend Deep Learning in Security market dynamics, the market study is analysed across major geographical regions/country
– North America: United States, Canada, and Mexico
– South & Central America: Argentina, Chile, Brazil and Others
– Middle East & Africa: Saudi Arabia, UAE, Israel, Turkey, Egypt, South Africa & Rest of MEA.
– Europe: UK, France, Italy, Germany, Spain, BeNeLux, Russia, NORDIC Nations and Rest of Europe.
– Asia-Pacific: India, China, Japan, South Korea, Indonesia, Thailand, Singapore, Australia and Rest of APAC.
Important Years in Deep Learning in Security Market Study Major trends of Deep Learning in Security Market using final data for 2019 and previous years, as well as quarterly or annual reports for 2021. In general, Years considered in the study i.e., base year as 2020, Historical data considered as 2015-2020 and Forecast time frame is 2021-2027.
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The Deep Learning in Security study is a perfectly designed with mix of both statistically relevant quantitative data from industry, coupled with insightful qualitative comment and analysis from Industry experts and consultants. To ascertain a deeper view; Deep Learning in Security Market Size by key business segments and applications for each of above listed region/country is provided along with competitive landscape that includes Comparative Market Share Analysis by Players (M USD) (2021-2027) and market concentration rate of Deep Learning in Security Industry in 2021.
In-depth company profiles for 15+ Deep Learning in Security leading and emerging players that covers 3-years financial history, swot analysis and other vital information like legal name, website, headquarter, % market share and position, distribution and marketing channels and latest developments.
Driving and maintaining growth continues to be a top-of mind issue for Boards, CXOs, and investors in the Technology industry. Deep Learning in Security companies and the chain of services supporting them are facing profound business challenges majorly from three factors:
1. The explosive rate at which competitors and Deep Learning in Security industry is growing.
2. The amount of growth that is driven by innovation in technologies, value propositions, products and services.
3. The speed at which innovations needs to be furnished in order to drive growth in Deep Learning in Security Market.
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IBM recently developed three artificial intelligence tools that could help medical researchers fight cancer.
Now, the company has decided to make all three tools open-source, meaning scientists will be able to use them in their research whenever they please, according to ZDNet. The tools are designed to streamline the cancer drug development process and help scientists stay on top of newly-published research — so, if they prove useful, it could mean more cancer treatments coming through the pipeline more rapidly than before.
This week, IBM scientists are presenting the three AI tools at two molecular biology conferences in Switzerland, according to an IBM press release.
The first, PaccMann, uses deep learning algorithms to predict whether compounds will be viable anticancer drugs, taking some of the expensive guesswork out of pharmaceutical development, according to the press release. Then there's INtERAcT, which automatically parses medical journals for important updates in the field, and PIMKL, which helps doctors provide tailor-fit care based on individual patients' needs.
"Our goal is to deepen our understanding of cancer to equip industries and academia with the knowledge that could potentially one day help fuel new treatments and therapies," IBM wrote in the release.
The company recently came under fire when its Watson "AI Doctor" offered dangerous medical advice, but the three new projects are entirely separate from that project.
READ MORE: IBM gives cancer-killing drug AI project to the open source community [ZDNet]
More on IBM: Doctors Are Losing Faith in IBM Watson’s AI Doctor
IBM’s annual Cost of Data Breach Report for 2022 is packed with revelations, and as usual none of them are good news. Headlining the report is the record-setting cost of data breaches, with the global average now at $4.35 million. The report also reveals that much of that expense comes with the data breach version of “long Covid,” expenses that are realized more than a year after the attack.
Most organizations (60%) are passing these added costs on to consumers in the form of higher prices. And while 83% of organizations now report experiencing at least one data breach, only a small minority are adopting zero trust strategies.
The IBM report draws on input from 550 global organizations surveyed about the period between March 2021 and March 2022, in partnership with the Ponemon Institute.
Though the average cost of a data breach is up, it is only by about 2.6%; the average in 2021 was $4.24 million. This represents a total climb of 13% since 2020, however, reflecting the general spike in cyber crime seen during the pandemic years.
Organizations are also increasingly not opting to absorb the cost of data breaches, with the majority (60%) compensating by raising consumer prices separate from any other accurate increases due to inflation or supply chain issues. The report indicates that this may be an underreported upward influence on prices of consumer goods, as 83% of organizations now say that they have been breached at least once.
Brad Hong, Customer Success Manager for Horizon3.ai, sees a potential consumer backlash on the horizon once public awareness of this practice grows: “It’s already a breach of confidence to lose the confidential data of customers, and sure there’s bound to be an organization across those surveyed who genuinely did put in the effort to protect against and curb attacks, but for those who did nothing, those who, instead of creating a disaster recovery plan, just bought cyber insurance to cover the org’s operational losses, and those who simply didn’t care enough to heed the warnings, it’s the coup de grâce to then pass the cost of breaches to the same customers who are now the victims of a data breach. I’d be curious to know what percent of the 60% of organizations who increased the price of their products and services are using the extra revenue for a war chest or to actually reinforce their security—realistically, it’s most likely just being used to fill a gap in lost revenue for shareholders’ sake post-breach. Without government regulations outlining restrictions on passing cost of breach to consumer, at the least, not without the honest & measurable efforts of a corporation as their custodian, what accountability do we all have against that one executive who didn’t want to change his/her password?”
Breach costs also have an increasingly long tail, as nearly half now come over a year after the date of the attack. The largest of these are generally fines that are levied after an investigation, and decisions or settlements in class action lawsuits. While the popular new “double extortion” approach of ransomware attacks can drive long-term costs in this way, the study finds that companies paying ransom demands to settle the problem quickly aren’t necessarily seeing a large amount of overall savings: their average breach cost drops by just $610,000.
Sanjay Raja, VP of Product with Gurucul, expands on how knock-on data breach damage can continue for years: “The follow-up attack effect, as described, is a significant problem as the playbooks and solutions provided to security operations teams are overly broad and lack the necessary context and response actions for proper remediation. For example, shutting down a user or application or adding a firewall block rule or quarantining a network segment to negate an attack is not a sustainable remediation step to protect an organization on an ongoing basis. It starts with a proper threat detection, investigation and response solution. Current SIEMs and XDR solutions lack the variety of data, telemetry and combined analytics to not only identify an attack campaign and even detect variants on previously successful attacks, but also provide the necessary context, accuracy and validation of the attack to build both a precise and complete response that can be trusted. This is an even greater challenge when current solutions cannot handle complex hybrid multi-cloud architectures leading to significant blind spots and false positives at the very start of the security analyst journey.”
In spite of over four out of five organizations now having experienced some sort of data breach, only slightly over 20% of critical infrastructure companies have moved to zero trust strategies to secure their networks. Cloud security is also lagging as well, with a little under half (43%) of all respondents saying that their security practices in this area are either “early stage” or do not yet exist.
Those that have onboarded security automation and AI elements are the only group seeing massive savings: their average cost of data breach is $3.05 million lower. This particular study does not track average ransom demands, but refers to Sophos research that puts the most accurate number at $812,000 globally.
The study also notes serious problems with incident response plans, especially troubling in an environment in which the average ransomware attack is now carried out in four days or less and the “time to ransom” has dropped to a matter of hours in some cases. 37% of respondents say that they do not test their incident response plans regularly. 62% say that they are understaffed to meet their cybersecurity needs, and these organizations tend to suffer over half a million more dollars in damages when they are breached.
Of course, cost of data breaches is not distributed evenly by geography or by industry type. Some are taking much bigger hits than others, reflecting trends established in prior reports. The health care industry is now absorbing a little over $10 million in damage per breach, with the average cost of data breach rising by $1 million from 2021. And companies in the United States face greater data breach costs than their counterparts around the world, at over $8 million per incident.
Shawn Surber, VP of Solutions Architecture and Strategy with Tanium, provides some insight into the unique struggles that the health care industry faces in implementing effective cybersecurity: “Healthcare continues to suffer the greatest cost of breaches but has among the lowest spend on cybersecurity of any industry, despite being deemed ‘critical infrastructure.’ The increased vulnerability of healthcare organizations to cyber threats can be traced to outdated IT systems, the lack of robust security controls, and insufficient IT staff, while valuable medical and health data— and the need to pay ransoms quickly to maintain access to that data— make healthcare targets popular and relatively easy to breach. Unlike other industries that can migrate data and sunset old systems, limited IT and security budgets at healthcare orgs make migration difficult and potentially expensive, particularly when an older system provides a small but unique function or houses data necessary for compliance or research, but still doesn’t make the cut to transition to a newer system. Hackers know these weaknesses and exploit them. Additionally, healthcare orgs haven’t sufficiently updated their security strategies and the tools that manufacturers, IT software vendors, and the FDA have made haven’t been robust enough to thwart the more sophisticated techniques of threat actors.”
Familiar incident types also lead the list of the causes of data breaches: compromised credentials (19%), followed by phishing (16%). Breaches initiated by these methods also tended to be a little more costly, at an average of $4.91 million per incident.
Though the numbers are never as neat and clean as averages would indicate, it would appear that the cost of data breaches is cut dramatically for companies that implement solid automated “deep learning” cybersecurity tools, zero trust systems and regularly tested incident response plans. Mature cloud security programs are also a substantial cost saver.
Discover why IBM ranked second highest in the Data Fabric Use Case according to Gartner analysts.
“A data fabric enables faster access to trusted data across distributed landscapes by utilising active metadata, semantics and machine learning (ML) capabilities. It is an emerging design; data fabric isn’t a common use case in the market yet. We picked it as a forward-facing use case — not every vendor has the full set of capabilities to deliver a data fabric design.”
Download this free Gartner® report to see how IBM is addressing this emerging design and has ranked second highest in the Data Fabric use case.