Category Archives: Ai

Recent strides in the efficacy of AI, the adoption of IoT devices and the power of edge computing have come together to unlock the power of edge AI.

This has opened new opportunities for edge AI that were previously unimaginable from helping radiologists identify pathologies in the hospital, to driving cars down the freeway, to helping us pollinate plants.

Countless analysts and businesses are talking about and implementing edge computing, which traces its origins to the 1990s, when content delivery networks were created to serve web and video content from edge servers deployed close to users.

Today, almost every business has job functions that can benefit from the adoption of edge AI. In fact, edge applications are driving the next wave of AI in ways that improve our lives at home, at work, in school and in transit.

Learn more about what edge AI is, its benefits and how it works, examples of edge AI use cases, and the relationship between edge computing and cloud computing.

Edge AI is the deployment of AI applications in devices throughout the physical world. Its called edge AI because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center.

Since the internet has global reach, the edge of the network can connote any location. It can be a retail store, factory, hospital or devices all around us, like traffic lights, autonomous machines and phones.

Organizations from every industry are looking to increase automation to improve processes, efficiency and safety.

To help them, computer programs need to recognize patterns and execute tasks repeatedly and safely. But the world is unstructured and the range of tasks that humans perform covers infinite circumstances that are impossible to fully describe in programs and rules.

Advances in edge AI have opened opportunities for machines and devices, wherever they may be, to operate with the intelligence of human cognition. AI-enabled smart applications learn to perform similar tasks under different circumstances, much like real life.

The efficacy of deploying AI models at the edge arises from three recent innovations.

Since AI algorithms are capable of understanding language, sights, sounds, smells, temperature, faces and other analog forms of unstructured information, theyre particularly useful in places occupied by end users with real-world problems. These AI applications would be impractical or even impossible to deploy in a centralized cloud or enterprise data center due to issues related to latency, bandwidth and privacy.

The benefits of edge AI include:

For machines to see, perform object detection, drive cars, understand speech, speak, walk or otherwise emulate human skills, they need to functionally replicate human intelligence.

AI employs a data structure called a deep neural network to replicate human cognition. These DNNs are trained to answer specific types of questions by being shown many examples of that type of question along with correct answers.

This training process, known as deep learning, often runs in a data center or the cloud due to the vast amount of data required to train an accurate model, and the need for data scientists to collaborate on configuring the model. After training, the model graduates to become an inference engine that can answer real-world questions.

In edge AI deployments, the inference engine runs on some kind of computer or device in far-flung locations such as factories, hospitals, cars, satellites and homes. When the AI stumbles on a problem, the troublesome data is commonly uploaded to the cloud for further training of the original AI model, which at some point replaces the inference engine at the edge. This feedback loop plays a significant role in boosting model performance; once edge AI models are deployed, they only get smarter and smarter.

AI is the most powerful technology force of our time. Were now at a time where AI is revolutionizing the worlds largest industries.

Across manufacturing, healthcare, financial services, transportation, energy and more, edge AI is driving new business outcomes in every sector, including:

AI applications can run in a data center like those in public clouds, or out in the field at the networks edge, near the user. Cloud computing and edge computing each offer benefits that can be combined when deploying edge AI.

The cloud offers benefits related to infrastructure cost, scalability, high utilization, resilience from server failure, and collaboration. Edge computing offers faster response times, lower bandwidth costs and resilience from network failure.

There are several ways in which cloud computing can support an edge AI deployment:

Learn more about the best practices for hybrid edge architectures.

Thanks to the commercial maturation of neural networks, proliferation of IoT devices, advances in parallel computation and 5G, there is now robust infrastructure for generalized machine learning. This is allowing enterprises to capitalize on the colossal opportunity to bring AI into their places of business and act upon real-time insights, all while decreasing costs and increasing privacy.

We are only in the early innings of edge AI, and still the possible applications seem endless.

Learn how your organization can deploy edge AI by checking out the top considerations for deploying AI at the edge.

Link:

What Is Edge AI and How Does It Work? - NVIDIA Blog

DUBLIN--(BUSINESS WIRE)--The "AI in Computer Vision Market by Technology, Solutions, Use Cases, Deployment Model and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

This report assesses the application of AI in computer vision systems used in conjunction with connected devices, hardware components, embedded software, AI platforms, and analytics. The report analyzes machine learning models and APIs used in computer vision systems along with the application of neural networks in AI analytics systems.

This research also evaluates the causal relationship of computer vision systems with IoT, Edge computing, and connected machines along with core hardware and software technology. The report also analyzes the relation of emotion AI with computer vision systems along with the market factors.

Select Report Findings:

Computer vision systems are dedicated to simulate the human visual system while analyzing the information extracted from photos and videos. They do this by way of mathematical operations in conjunction with signal processing systems to process both digital and analog images. These systems leverage both two dimensional and three-dimensional processes.

AI represents the ability to organize information and create outcomes in learning, decision-making, and problem-solving using a computer-enabled robotic system in the same way a human brain does. The integration of AI and computer vision systems enhance the accuracy of object identification, classification, and analysis of information.

Through leveraging AI, computer vision systems provide a robotic system in which vision sensing capabilities provide information about the environment. One of the best examples of this in practice is autonomous vehicles, which rely on computer vision and AI-based decision making for safe travel.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

2.1 Defining AI in Computer Vision

2.2 Artificial General Intelligence and Super Intelligence

2.3 AI and Computer Vision Market Predictions

2.4 AI Outcomes and Enterprise Benefits

2.5 Cognitive Computing and Swarm Intelligence

2.6 Market Driver and Opportunity Analysis

2.7 Market Challenge Analysis

2.8 Covid-19 Impact

2.9 Value Chain Analysis

2.10 Pricing Analysis

2.11 Hs Code 854231

2.12 AI Patent and Regulatory Framework

2.13 AI Public Policy Issues

3.0 Technology and Application Analysis

3.1 Technology Analysis

3.2 IoT Device Ecosystem: Consumer, Enterprise, Industrial, and Government

3.3 Machine Learning Model

3.4 Artificial Neural Networks

3.5 Emotion AI Analysis

3.6 Edge Computing and 5G Networks

3.7 Smart Machine and Virtual Twinning

3.8 Factory Automation and Industry 4.0

3.9 Building Automation and Smart Workplace

3.10 Cloud Robotics and Public Security

3.11 Predictive 3D Design

3.12 IoT Application and Big Data Analytics

3.13 AI Application Delivery Platforms

3.14 Enterprise Adoption and External Investment

3.15 Application and Industry Vertical Analysis

3.16 Use Case Analysis

4.0 Company Analysis

5.0 AI in Computer Vision Market Analysis and Forecasts 2022 - 2027

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/yo9y26

About ResearchAndMarkets.com

ResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

The rest is here:

Global Market for AI in Computer Vision to Reach $73.7 Billion by 2027 - ResearchAndMarkets.com - Business Wire

Working to transform cognitive testing to help millions around the world, the Cogentivity team has developed a healthcare solution that spots early signs of cognitive impairments in individuals.Cognetivity was founded as Sina Habibi and Seyed-Mahdi Khaligh-Razavi bonded over how dementia had touched their lives while studying for PhDs at the University of Cambridge. The pair shared an overwhelming feeling that their loved ones had been diagnosed too late to reduce the diseases devastating toll. Recognising an issue in dementia diagnosis, they set up Cognetivity to fix it.

With deep sector experience in neuroscience, AI, medicine and neurology, AI Magazine speaks to Mazen Sobh from Cognetivity to learn how the company is delivering a new paradigm in global healthcare.

"Cognetivity Neurosciences is a company that was established in the UK and is a spinoff from Cambridge University. From the university, two scientists PhD students collaborated and developed an application that specialises in identifying early or subtle signs of cognitive impairments in individuals. This application, CognICA, which can be used on an iPad or smartphone, combines cutting-edge neuroscience with artificial intelligence. The technology which has been patented and also peer-reviewed, displays 100 images in quick succession, asking patients to simply identify whether they see an animal in each or not. Due to the fear or food response, humans react strongly to animal imagery, activating large areas of their brain. AI analysis compares their reactions to how others in their age group responded and this generates a result that indicates how healthy a patient's brain is when compared to normal, mildly impaired and severely impaired individuals.

"I am the Vice President for Commerical Development and Im based in Dubai. I'm responsible for the growth of the business in the Middle East with further plans to expand to the Far East and beyond."

"Our CognICA solution is a technology that empowers clinicians to make better decisions that can dramatically improve patient outcomes. We want the solution to be the de facto assessment tool for clinicians who want to look at cognitive behaviour and cognitive development for humans. The company has also developed a mobile app called OptiMind that can be downloaded on your iPhone. This app is a wellness app that objectively measures your everyday cognitive performance for personal use. OptiMind can highlight personal lifestyle correlations with your cognitive sharpness and provide scope for optimisation.

"Through this application, we provide a graphical view of the relationship between a users lifestyle and mental wellbeing. It allows users to assess what factors improve their cognitive performance and empowers them to alter their lifestyle choices to optimise their mental health."

See more here:

Cognetivity's AI solutions to transform cognitive testing - Healthcare Global - Healthcare News, Magazine and Website

When British artist Harold Cohen met his first computer in 1968, he wondered if the machine might help solve a mystery that had long puzzled him: How can we look at a drawing, a few little scribbles, and see a face? Five years later, he devised a robotic artist called AARON to explore this idea. He equipped it with basic rules for painting and for how body parts are represented in portraiture and then set it loose making art.

Not far behind was the composer David Cope, who coined the phrase musical intelligence to describe his experiments with artificial intelligencepowered composition. Cope once told me that as early as the 1960s, it seemed to him perfectly logical to do creative things with algorithms rather than to painstakingly draw by hand every word of a story, note of a musical composition or brush stroke of a painting. He initially tinkered with algorithms on paper, then in 1981 moved to computers to help solve a case of composers block.

Cohen and Cope were among a handful of eccentrics pushing computers to go against their nature as cold, calculating things. The still-nascent field of AI had its focus set squarely on solid concepts like reasoning and planning, or on tasks like playing chess and checkers or solving mathematical problems. Most AI researchers balked at the notion of creative machines.

Slowly, however, as Cohen and Cope cranked out a stream of academic papers and books about their work, a field emerged around them: computational creativity. It included the study and development of autonomous creative systems, interactive tools that support human creativity and mathematical approaches to modeling human creativity. In the late 1990s, computational creativity became a formalized area of study with a growing cohort of researchers and eventually its own journal and annual event.

Soon enough thanks to new techniques rooted in machine learning and artificial neural networks, in which connected computing nodes attempt to mirror the workings of the brain creative AIs could absorb and internalize real-world data and identify patterns and rules that they could apply to their creations.

Computer scientist Simon Colton, then at Imperial College London and now at Queen Mary University of London and Monash University in Melbourne, Australia, spent much of the 2000s building the Painting Fool. The computer program analyzed the text of news articles and other written works to determine the sentiment and extract keywords. It then combined that analysis with an automated search of the photography website Flickr to help it generate painterly collages in the mood of the original article. Later the Painting Fool learned to paint portraits in real time of people it met through an attached camera, again applying its mood to the style of the portrait (or in some cases refusing to paint anything because it was in a bad mood).

Similarly, in the early 2010s, computational creativity turned to gaming. AI researcher and game designer Michael Cook dedicated his Ph.D. thesis and early research associate work at Goldsmiths, University of London to creating ANGELINA which made simple games based on news articles from The Guardian, combining current affairs text analysis with hard-coded design and programming techniques.

During this era, Colton says, AIs began to look like creative artists in their own right incorporating elements of creativity such as intentionality, skill, appreciation and imagination. But what followed was a focus on mimicry, along with controversy over what it means to be creative.

New techniques that excelled at classifying data to high degrees of precision through repeated analysis helped AI master existing creative styles. AI could now create works like those of classical composers, famous painters, novelists and more.

One AI-authored painting modeled on thousands of portraits painted between the 14th and 20th centuries sold for $432,500 at auction. In another case, study participants struggled to differentiate the musical phrases of Johann Sebastian Bach from those created by a computer program called Kulitta that had been trained on Bachs compositions. Even IBM got in on the fun, tasking its Watson AI system with analyzing 9,000 recipes to devise its own cuisine ideas.

But many in the field, as well as onlookers, wondered if these AIs really showed creativity. Though sophisticated in their mimicry, these creative AIs seemed incapable of true innovation because they lacked the capacity to incorporate new influences from their environment. Colton and a colleague described them as requiring much human intervention, supervision, and highly technical knowledge in producing creative results. Overall, as composer and computer music researcher Palle Dahlstedt puts it, these AIs converged toward the mean, creating something typical of what is already out there, whereas creativity is supposed to diverge away from the typical.

Headlines and summaries of the latest Science News articles, delivered to your inbox

Thank you for signing up!

There was a problem signing you up.

In order to make the step to true creativity, Dahlstedt suggested, AI would have to model the causes of the music, the conditions for its coming into being not the results.

True creativity is a quest for originality. It is a recombination of disparate ideas in new ways. It is unexpected solutions. It might be music or painting or dance, but also the flash of inspiration that helps lead to advances on the order of light bulbs and airplanes and the periodic table. In the view of many in the computational creativity field, it is not yet attainable by machines.

In just the past few years, creative AIs have expanded into style invention into authorship that is individualized rather than imitative and that projects meaning and intentionality, even if none exists. For Colton, this element of intentionality a focus on the process, more so than the final output is key to achieving creativity. But he wonders whether meaning and authenticity are also essential, as the same poem could lead to vastly different interpretations if the reader knows it was written by a man versus a woman versus a machine.

If an AI lacks the self-awareness to reflect on its actions and experiences, and to communicate its creative intent, then is it truly creative? Or is the creativity still with the author who fed it data and directed it to act?

Ultimately, moving from an attempt at thinking machines to an attempt at creative machines may transform our understanding of ourselves. Seventy years ago Alan Turing sometimes described as the father of artificial intelligence devised a test he called the imitation game to measure a machines intelligence against our own. Turings greatest insight, writes philosopher of technology Joel Parthemore of the University of Skvde in Sweden, lie in seeing digital computers as a mirror by which the human mind could consider itself in ways that previously were not possible.

Go here to see the original:

Artificial intelligence challenges what it means to be creative - Science News Magazine

The quality of AI-generated voices has improved rapidly in recent years, but there are still aspects of human speech that escape synthetic imitation. Sure, AI actors can deliver smooth corporate voiceovers for presentations and adverts, but more complex performances a convincing rendition of Hamlet, for example remain out of reach.

Sonantic, an AI voice startup, says its made a minor breakthrough in its development of audio deepfakes, creating a synthetic voice that can express subtleties like teasing and flirtation. The company says the key to its advance is the incorporation of non-speech sounds into its audio; training its AI models to recreate those small intakes of breath tiny scoffs and half-hidden chuckles that give real speech its stamp of biological authenticity.

We chose love as a general theme, Sonantic co-founder and CTO John Flynn tells The Verge. But our research goal was to see if we could model subtle emotions. Bigger emotions are a little easier to capture.

In the video below, you can hear the companys attempt at a flirtatious AI though whether or not you think it captures the nuances of human speech is a subjective question. On a first listen, I thought the voice was near-indistinguishable from that of a real person, but colleagues at The Verge say they instantly clocked it as a robot, pointing to the uncanny spaces left between certain words, and a slight synthetic crinkle in the pronunciation.

Sonantic CEO Zeena Qureshi describes the companys software as Photoshop for voice. Its interface lets users type out the speech they want to synthesize, specify the mood of the delivery, and then select from a cast of AI voices, most of which are copied from real human actors. This is by no means a unique offering (rivals like Descript sell similar packages) but Sonantic says its level of customization is more in-depth than that of rivals.

Emotional choices for delivery include anger, fear, sadness, happiness, and joy, and, with this weeks update, flirtatious, coy, teasing, and boasting. A director mode allows for even more tweaking: the pitch of a voice can be adjusted, the intensity of delivery dialed up or down, and those little non-speech vocalizations like laughs and breaths inserted.

I think thats the main difference our ability to direct and control and edit and sculpt a performance, says Flynn. Our clients are mostly triple-A game studios, entertainment studios, and were branching out into other industries. We recently did a partnership with Mercedes [to customize its in-car digital assistant] earlier this year.

As is often the case with such technology, though, the real benchmark for Sonantics achievement is the audio that comes fresh out of its machine learning models, rather than whats used in polished, PR-ready demos. Flynn says the speech synthesized for its flirty video required very little manual adjustment, but the company did cycle through a few different renderings to find the very best output.

To try and get a raw and representative sample of Sonantics technology, I asked them to render the same line (directed to you, dear Verge reader) using a handful of different moods. You can listen to them yourself to compare.

First, heres flirty:

Then teasing:

Pleased:

Cheerful:

And finally, casual:

To my ears, at least, these clips are a lot rougher than the demo. This suggests a few things. First, that manual polishing is needed to get the most out of AI voices. This is true of many AI endeavors, like self-driving cars, which have successfully automated very basic driving but still struggle with that last and all-important 5 percent that defines human competence. It means that fully-automated, totally-convincing AI voice synthesis is still a way off.

Second, I think it shows that the psychological concept of priming can do a lot to trick your senses. The video demo with its footage of a real human actor being unsettlingly intimate towards the camera may cue your brain to hear the accompanying voice as real. The best synthetic media, then, might be that which combines real and fake outputs.

Apart from the question of how convincing the technology is, Sonantics demo raises other issues like, what are the ethics of deploying a flirtatious AI? Is it fair to manipulate listeners in this way? And why did Sonantic choose to make its flirting figure female? (Its a choice that arguably perpetuates a subtle form of sexism in the male-dominated tech industry, where companies tend to code AI assistants as pliant even flirty secretaries.)

On the first question, the company said their choice of a female voice was simply inspired by Spike Jonzes 2013 film Her, where the protagonist falls in love with a female AI assistant named Samantha. On the second, Sonantic said it recognizes the ethical quandaries that accompany the development of new technology, and that its careful in how and where it uses its AI voices.

Thats one of the biggest reasons weve stuck to entertainment, says CEO Qureshi. CGI isnt used for just anything its used for the best entertainment products and simulations. We see this [technology] the same way. She adds that all of the companys demos include a disclosure that the voice is, indeed, synthetic (though this doesnt mean much if clients want to use the companys software to generate voices for more deceitful purposes).

Comparing AI voice synthesis to other entertainment products makes sense. After all, being manipulated by film and TV is arguably the reason we make those things in the first place. But there is also something to be said about the fact that AI will allow such manipulation to be deployed at scale, with less attention to its impact in individual cases. Around the world, for example, people are already forming relationships even falling in love with AI chatbots. Adding AI-generated voices to these bots will surely make them more potent, raising questions about how these and other systems should be engineered. If AI voices can convincingly flirt, what might they persuade you to do?

The rest is here:

Listen to an AI voice actor try and flirt with you - The Verge

RJ Pierce, Tech Times 17 February 2022, 07:02 am

AI has been the subject of countless popular TV shows and movies over the years-just not in a relatively positive way. In these shows, it always seems like artificial intelligence will decide to completely wipe out humanity and civilization from existence. It's a bleak "prediction," but does it actually have any basis in reality?

(Photo : Photo credit should read BEN STANSALL/AFP via Getty Image)An AI robot with a humanistic face, entitled Alter 3: Offloaded Agency, is pictured during a photocall to promote the forthcoming exhibition entitled "AI: More than Human", at the Barbican Centre in London on May 15, 2019. - Managing the health of the planet, fighting against discrimination, innovating in the arts: the fields in which artificial intelligence (AI) can help humanity are innumerable.

According to several scientists, the feared dangers of AI aren't much of an existential threat to humanity as a whole. And that depends on one thing: whether it is even possible for us to create artificial intelligence way smarter than we are, writes ScienceAlert.

The AI that exists right now is pretty powerful in its own right. It is what's being used for things like self-driving cars, facial recognition software, and even Google recommendations. But the thing with current-gen AI is that it's considered "narrow" or "weak."

While this kind of artificial intelligence is already quite good, they're often only capable of doing one thing exceptionally, according to LabRoots. If you try to make them do something else while doing something they're good at, these AIs will fail because they lack the necessary data to perform it.

(Photo : geralt from Pixabay)

Current-generation artificial intelligence still falls short of tasks that will always require abilities that only humans possess, writes Forbes. For instance, experienced surgeons are still the best choice for performing surgeries, with their fine motor skills and skill at perceiving individual situations.

You also can't use an AI to replace HR professionals, because the job will require a deep, intrinsic understanding of human reactions that a machine just doesn't have, no matter how "advanced" it might be. It is these kinds of situations where combining machine and human intelligence still reigns supreme. The human element provides the machine with the necessary context, while the latter is put to work crunching numbers and giving recommendations.

Read Also: A Robot That Can 'Think' Has Just Been Created--Here Are The Implications

In an article by The Conversation, they put this specific argument forward. A machine can always "learn" if it is fed data about the task it's meant to achieve. Sure, it can process information much faster than a human can (and perhaps even come up with solutions no person can ever think of), but it doesn't make the machine smarter than a human at all.

Here's one situation where machine learning is still way behind human learning. Take a toddler, for instance. That child can learn how to do a specific task within seconds just by watching somebody do it. A machine can only learn something if it is fed an extremely massive amount of data, which it uses when performing trial-and-error.

At the end of the day, it still falls on the human element of the issue. You should be far more scared of how humans use artificial intelligence, and not the AI itself. This is considering the technology's capability to draw conclusions from whatever data is being fed to it and how it can only focus on one task at a time.

(Photo : Getty Images )

In other words, an AI trained to do something good, like identifying climate change tipping points, is not dangerous at all. But a machine which is trained in something bad, like warfare, can be extremely perilous. So don't be scared of robots taking over the world, because people-not the perceived dangers of AI-will still be the most critical aspect of civilization's downfall.

Related Article: 'Free Guy' Artificial Intelligence: Can Tech Like This Actually Exist?

This article is owned by Tech Times

Written by RJ Pierce

2021 TECHTIMES.com All rights reserved. Do not reproduce without permission.

Read the rest here:

AI Will NOT Take Over The World And Drive Humanity To Extinction--Here's Why - Tech Times

Topics AI in Action

This column series looks at the biggest data and analytics challenges facing modern companies and dives deep into successful use cases that can help other organizations accelerate their AI progress.

With the start of each year come predictions, plans, and surveys from consulting firms. When it comes to artificial intelligence, multiple recent surveys indicate that companies arent just planning on spending serious money on AI in 2022 they are already making good money from the technology.

A bit of context might be helpful. Despite some AI successes, one of the challenges in recent years has been that projects involving the technology have frequently lacked sufficient economic returns. In a 2019 MIT Sloan Management Review and Boston Consulting Group AI survey, for example, 7 out of 10 companies reported minimal or no value from their AI investments. One of the reasons for poor returns was that relatively few projects were deployed into production; they were too often research exercises. Production deployments admittedly can be difficult, since they usually require integration with existing systems and processes, worker reskilling, and the ability to scale AI technology.

Get Updates on Leading With AI and Data

Get monthly insights on how artificial intelligence impacts your organization and what it means for your company and customers.

Please enter a valid email address

Thank you for signing up

Privacy Policy

Just a few years later, things are beginning to change. In the 2022 survey of senior data and technology executives by NewVantage Partners (where Randy Bean is CEO and cofounder, and Tom Davenport is a fellow), 92% of large companies reported that they are achieving returns on their data and AI investments. Thats up markedly from 48% in 2017. The same percentage (92%) said that they are increasing investments in data and AI, equaling last years percentage. Twenty-six percent of companies have AI systems in widespread production more than double the 12% in last years survey. The survey also asked respondents whether their organizations were data driven, and only 26% said they are. However, that doesnt seem to be preventing them from making progress on AI.

The NewVantage survey respondents largely represent North American companies. But other surveys suggest that companies around the globe are also registering more value with AI. The State of AI in the Enterprise survey by Deloitte (where Tom is a senior adviser to the AI practice), fielded in mid-2021, found that two types of companies are getting value from their investments. Twenty-eight percent of survey respondents were classified as transformers companies reporting high business outcomes and a relatively high number of production AI deployments (six on average). This group has identified and largely adopted leading practices associated with the strongest AI outcomes, including having an AI strategy, building an ecosystem around AI, and putting organizational structures and processes in place (such as machine learning operations, or MLOps) to keep AI on track.

The other group getting value, accounting for 26% of respondents, was labeled pathseekers. They reported high outcomes but a lower number of deployments. They have also adopted capabilities and behaviors that have led to success with AI, but on fewer projects. They have not scaled to the same degree as transformers.

Still, thats more than half of the global respondents reporting positive business outcomes from AI. As weve noted, its difficult or impossible to benefit from AI without deploying it, but these results suggest that you dont need a lot of deployments to get value.

A 2021 McKinsey global survey on AI also found that AI adoption and value are increasing. McKinsey found that the number of companies reporting AI adoption in at least one function had increased to 56%, up from 50% in 2020. More importantly, the survey also indicates that AIs economic return is growing. The share of respondents reporting at least 5% of earnings (EBIT) that are attributable to AI has increased to 27%, up from 22% in the previous survey. Were not sure how survey respondents would calculate the percentage of earnings attributable to AI, but their responses do suggest high value.

Respondents to the McKinsey survey also reported significantly greater cost savings from AI than they did previously in every function, with the greatest improvements coming in product and service development, marketing and sales, and strategy and corporate finance.

And echoing the Deloitte survey, McKinsey found that progressive AI practices are being rewarded. Companies seeing the biggest earnings increases from AI were not only following practices that lead to success, including MLOps, but also spending more efficiently on AI and taking advantage of cloud technologies to a greater extent.

A survey by IBM offers some insight into the impact of the COVID-19 pandemic on AI adoption, with a particular focus on automation-oriented technologies. It found that 80% of companies are already using some form of automation technology or plan to do so over the next year. Just over a third of the organizations surveyed said that the pandemic influenced their decision to adopt and use automation as a means of improving productivity. The respondents to the IBM survey were IT professionals, which may have influenced the results; IT process automation (known as AI for IT operations, or AIOps) is a popular use case for the technology.

We should also mention an interesting 2021 survey conducted by MIT Sloan Management Review and Boston Consulting Group that set out to assess not the monetary benefits of AI but its cultural enhancements. Because no one (to our knowledge) has asked these types of questions before, we cant make comparisons to the past.

In that global survey, 58% of all respondents who had participated in an AI implementation agreed that their AI solutions improved efficiency and decision-making among teams. A majority of that group (78%) also reported improved collaboration within teams. Are improved decision-making and collaboration indicators of cultural benefit? Were not sure, but they could certainly translate into economic value.

The survey also found that AI yields strategic benefits, but they mostly accrued to companies that use AI to explore new ways of creating value rather than cutting costs. Those that used AI primarily to create new value were 2.5 times more likely to feel that AI is helping their company competitively compared with those that said they are using AI primarily to improve existing processes; they were also 2.7 times more likely to agree that AI helps capture opportunities in adjacent industries. Its easy to see how these traits could turn into economic value.

For those who want the current AI spring to bloom forever, this is all great news. There is still substantial room for improvement in the economic returns from AI, of course, and these surveys tap only subjective perceptions. The biggest remaining stumbling block, according to a recent small survey of data scientists, is that the majority of machine learning models are still not deployed in production environments within organizations. Companies and AI leaders still need to work on this issue.

However, the fact that so many business leaders responding to so many surveys on the topic feel that their organizations are capturing substantial value from AI is a definite improvement over the recent past, and a strong sign that AI is here to stay in the business landscape.

This column series looks at the biggest data and analytics challenges facing modern companies and dives deep into successful use cases that can help other organizations accelerate their AI progress.

Thomas H. Davenport (@tdav) is the Presidents Distinguished Professor of Information Technology and Management at Babson College, a visiting professor at Oxfords Sad Business School, and a fellow of the MIT Initiative on the Digital Economy. Randy Bean (@randybeannvp) is an industry thought leader, author, and CEO of NewVantage Partners, a strategic advisory company that is now a division of Wavestone, a global consultancy based in Paris. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).

See the original post here:

Companies Are Making Serious Money With AI - MIT Sloan

In the quest to make artificial intelligence that can reason and apply knowledge flexibly, many researchers are focused on fresh insights from neuroscience. Should they be looking to psychology too?

By Edd Gent

Micha Bednarski

ARTIFICIAL intelligence has come a long way. In recent years, smart machines inspired by the human brain have demonstrated superhuman abilities in games like chess and Go, proved uncannily adept at mimicking some of our language skills and mastered protein folding, a task too fiendishly difficult even for us.

But with various other aspects of what we might reasonably call human intelligence reasoning, understanding causality, applying knowledge flexibly, to name a few AIs still struggle. They are also woefully inefficient learners, requiring reams of data where humans need only a few examples.

Some researchers think all we need to bridge the chasm is ever larger AIs, while others want to turn back to natures blueprint. One path is to double down on efforts to copy the brain, better replicating the intricacies of real brain cells and the ways their activity is choreographed. But the brain is the most complex object in the known universe and it is far from clear how much of its complexity we need to replicate to reproduce its capabilities.

Thats why some believe more abstract ideas about how intelligence works can provide shortcuts. Their claim is that to really accelerate the progress of AI towards something that we can justifiably say thinks like a human, we need to emulate not the brain but the mind.

In some sense, theyre just different ways of looking at the same thing, but sometimes its profitable to do that, says Gary Marcus at New York University and start-up Robust AI. You dont want a replica, what you want is to learn the principles that allow the brain to be as effective as it is.

Read more:

Hybrid AI: A new way to make machine minds that really think like us - New Scientist

We are in the midst of profound change in the nature of employment worldwide. Fueled most recently by the pandemic, the nature of work, including our tools and practices, is undergoing dramatic changes. The Great Resignation, in part, reflects an understanding that many jobs do not have a viable future and that they are not best utilizing the abilities of the employees.

Jobs are on the verge of being altered or replaced by artificial intelligence and AI-assisted programs. Long gone are the years in which colleges prepared students for 30 years in a single career where few changes took place in the job itself. Turnover is rampant. The median length of time that wage and salary workers had been with their current employer was just 4.1 years, as reported by the U.S. Bureau of Labor Statistics in January 2020. Likely, that number has gone down further during the Great Resignation.

We are all familiar with the robotic revolution of prior decades in which assembly-line work positions were lost to robotic assembly lines. That shakeout took a toll on a skilled but less educated population. Human skills were no longer needed because intelligent robots could do the job faster, more consistently and at a lower cost to the business. Now, Brookings warns us that the most vulnerable jobs of the future are in better paid and better educated fields: Our analysis shows that workers with graduate or professional degrees will be almost four times as exposed to AI as workers with just a high school degree. Holders of bachelors degrees will be the most exposed by education level, more than five times as exposed to AI than workers with just a high school degree.

I understand that there is much more to be made of a college degree than merely a trade school preparing the student for work. However, in the current economy, it is abundantly clear that students seek jobs, career advancement and career potential far above all other forms of enrichment and perspective. Yet, as Jeff Selingo points out, The world of work has changed, while colleges, along with employers, are living in a different era. Its nearly impossible anymore for colleges to arm students with the vocational hard skills theyll need to last more than a few years in almost any job after graduation. Most of college graduates 20s are spent moving from job to job to further their education and learn additional skills. And the paradox is that job hopping is the primary reason employers are reluctant to invest in workers in the first place. That reluctance to invest in new workers is further fed by the advancement of less costly, more flexible and easily upgradable AI.

To a significant degree, the AI marketplace is responsive to shortfalls in the number of qualified workers. That is, where employers cannot find an adequate supply of humans to meet their needs, they will turn to AI. Boston Consulting Group sees the near-term future in this area is to engage government, companies and higher education:

To reduce the mismatch in skills, governments should update the education system. They should create more flexible institutions that can anticipate the future needs of companies and refocus on meta skills. Companies need to invest in corporate academies, training partnerships, and constant upskilling and reskilling of their existing workforces. They should also transform their HR functions and processes to cater to the shift in approach needed to hire and retain talent with the new skills in demand. Companies that make these investments and significant changes in their own processes stand to gain a substantial competitive advantage over those that stick with their current approach. Countries that leverage education to create attractive locations for companies will gain a competitive edge over their static neighbors.

Continuing and professional education is thriving both at universities and within the corporate environment. The rise of certificate programs is unprecedented. This is not taking place in a carefully orchestrated fashion. There is no semblance of an organized, concerted effort to identify emerging and future needs, assign specific standards across industries, and subsidize quality programs to meet the changing needs across the economy. Instead, there is more of a Wild West approach with individual universities and corporations creating their own entrepreneurial programs. In too few cases, states or corporate groups are trying to draft a road map to meet the learning needs in industries.

Meanwhile, artificial intelligence promises to transform 500million white collar jobs in the next five years! The higher educationindustry disconnect will take a huge toll on graduates in the workforce who have not been updated and upskilled for the emerging economy. This will further dilute the credibility and perceived value of degrees as they become increasingly outdated and irrelevant.

We need leadership within and across institutions to meet this challenge. A hodgepodge of credentials does not serve learners well. Clarity and specificity in outcomes as well as clear linkages to viable careers are needed to build effective paths of learning that meet the needs of today and tomorrow. Industry has already begun building its own education frameworks to meet its needs. Notably, the Google Career Certificate program has enrolled millions at an economical price point.

Who is leading the charge at your institution to respond to this massive shift in learning needs for our economy? Can you play a role in bringing coherence and meaningful alignment to inter-institutional/industrywide standards for certificate programs to meet the emerging new economy most effectively?

Read the rest here:

How AI and Associated Technologies Change the Role of Higher Ed - Inside Higher Ed

TALLINN, Estonia--(BUSINESS WIRE)--Cognitiwe, a new entrant to the growing AI market, prevents food waste through their predictive vision AI platform. We will be able to instantly monitor the freshness of vegetables and fruits in supermarkets, said Cognitiwe Co-Founder Attila Algan, adding that As well as checking for freshness of produce, shelf stock and planogram analysis, we are breathing new life into the retail sector with fraud detection.

Also, in manufacturing, our technology enables quality control and detects faulty products in production line. We also offer solutions for stock management, health and safety monitoring, explained Algan, adding: We want to position as a global brand, delivering our retail and manufacturing sector-specific products, developed using advanced technology on our predictive visual AI platform. Headquartered in Tallinn, Estonia, Cognitiwe has offices in Istanbul and Milan

Sustainability for Retail and Manufacturing

The United Nations Environment Programme (UNEP) 2021 Food Waste Index Report indicates that 13 percent of the 931 million tons of food waste generated worldwide in 2019 originated from the retail sector. "We will soon be able to use AI to reveal the environmental footprint of the retail sector's food waste that we have been able to prevent, Algan told reporters, adding further, and thanks to data from deep learning algorithms deployed on manufacturing production lines, we will achieve materials, time and energy savings that contribute to greater sustainability. Cognitiwe's GDPR compliant products can be integrated into existing IP cameras, without the need for additional hardware investment. Because it is cloud-based, they also do not require investments in servers.

Preventing financial losses in the retail and manufacturing sector

Cognitiwe Co-Founder Mete Bayrak notes clients, above all, by providing real time and predictive data, we help retail and manufacturing industries to reduce risks, prevent loss and improve quality. Bayrak added that they have introduced a feature to the retail product that prevents financial losses occurring as a result of the mis-scan or walk-off detection at supermarket checkouts and self-service payment points, nothing further that a 5G mobile solution for the health and safety of employees was also in its pilot phase.

See original here:

Cognitiwe Prevent the Food Waste With Their Predictive Vision AI Platform - Business Wire