Daily Archives: February 17, 2022

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?

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

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.

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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.

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

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.

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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.

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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).

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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.

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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?

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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.

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

If youve ever interacted online with a customer service rep who seemed highly responsive and polite, yet also incapable of nuanced conversation beyond basic queries, chances are you encountered an AI bot. And chances are, it was pretty obvious.

If investors have their way, however, theres a strong likelihood that in the future it will be much harder to distinguish between a bot and an actual human.

In recent years, venture and growth investors have poured billions into developers of customer service-focused automation technology. Companies in the the space are focused heavily on employing AI to speed resolutions of customer issues and reduce reliance on human agents.

The space saw a major funding boost today as Uniphore, a fast-growing provider of conversational automation to enterprises, announced a $400 million Series E funding round led by NEA. The financing brings total funding to date to $610 billion and sets a valuation of $2.5 billion for the 14-year-old, Palo Alto-headquartered company.

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Uniphore says its technology combines conversational AI, workflow automation and RPA (Robotic Process Automation) in a software offering that businesses can deploy in their customer service operations. Its tools enable businesses to fully automate some interactions, while others may loop in human representatives for certain tasks.

The company says it plans to use the funding for R&D in areas including voice AI, computer vision and tonal emotion, as well as to expand its business operations in North America, Europe and Asia Pacific.

This fundraising comes amid an active period for investment in companies developing technologies to automate customer interactions. A Crunchbase query of funding rounds for companies working on conversational automation, chatbots and related areas, showed more than $2.3 billion in venture, growth and private equity investment over the past two years.

In addition to Uniphore, some of the larger recent funding recipients include:

Illustration: Dom Guzman

Stay up to date with recent funding rounds, acquisitions, and more with the Crunchbase Daily.

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With $400M For Uniphore, Investors Affirm Conversations With AI Bots Here To Stay - Crunchbase News

Over the last decade, Artificial Intelligence has become an increasingly prevalent force in our everyday lives.

From consumer applications such as recommendations on Netflix and Spotify, to becoming a staple in the workplace with AI-based fraud detection, process automation, and cybersecurity. The near future indicates AI will further spread into every aspect of our lives. Its continued adoption and integration with new applications such as autonomous driving, healthcare, and others prompts IDC to project the global AI market to reach $550 billion by 2024.

This rapid growth, fueled by developments in deep learning, computer vision, and natural language processing, is continuously advancing through a combination of academic and Big Tech research groups such as Google, Facebook, AWS, OpenAI, among others. Thanks to the age of open-source, many of these advancements are available for public use.

Though promising, these developments in AI are not without limitations.

The Deployment Gap

While these collaborative open-source projects form the heart of the AI revolution, bringing AI into production is a complex, multi-step pipeline, each with its own challenges. From collecting and preparing data, experimentation and research, training and evaluation, to deployment and monitoring, each phase requires significant resources and expertise.

As noted in a recent survey: Many companies havent figured out how to achieve their ML/AI goals, bridging the gap between ML model building and practical deployments is still a challenging task. Theres a fundamental difference between building a model in a notebook and deploying an ML model into a production system that generates business value.

As such, an estimated~90% of ML models fail to make it to production.

Enter MLOps

As DevOps has significantly streamlined software development production, a new category of applications for improving the effectiveness of machine learning has risenMLOps - which by de definition is the set of practices at the intersection of Machine Learning, DevOps and Data Engineering. MLOps enables companies to innovate and bring products to market faster with greater efficiency. Though the precise definition of what is included in MLOps (vis--vis the traditional data stack or DevOps) can be open to interpretation, the current landscape encompasses hundreds of unique startups and prominent open-source projects seeking to tackle these challenges.

The Israeli MLOps Landscape

As with nearly every facet of technological advancement, there are a wealth of innovative Israeli MLOps-focused startups driving the area, many of which raised an aggregate hundreds of millions of dollars across the different segments in the space:

Data Preparation - Weve all heard the adage data is the new oil, which is very accurate in the context of AI. High quality data acts as the fuel for AI models; without it we receive a case of garbage-in garbage-out. Companies such as Monte Carlo and Databand provide reliability for data pipelines, ensuring quality data is consistently fed to the models, while open-source projects such as Treeverses LakeFS enable organizations to version their datasets that are shareable and reproducible across development teams. To increase model accuracy, Explorium, Datagen, and Datomize supplement an organizations existing data with external and synthetic data.

Model Development and Training While most ML models are based on open-source projects at their core, companies must fine-tune them to their specific needs and production environments to drive optimal results. Experimentation platforms like Comet provide data scientists with solutions to document, collaborate, and analyze model outputs, while organizations such as Deci optimize models to run with greater accuracy and less runtime vis--vis a developers specific hardware.

Deployment Platforms Commonplace to similar segmentations of technology, MLOps shares a best-of-suite vs. best-of-breed approach. Projects led by major cloud providers such as Googles KubeFlow, Databricks MLFlow, and AWS Sagemaker are the leading one-stop-shop solutions, but fall short in offering complete feature-sets. Innovating in this space, startups like Iguazio and Qwak offer holistic platforms that enable companies to build, deploy, and monitor their ML models.

Monitoring A segmentation with significant focus by Israeli startups, live production models require continuous monitoring and testing to identify drifts in precision and output. Several companies such as Aporia, Deepchecks, and Superwise ensure the integrity and efficiency of live models, continuously monitoring changes in underlying data or infrastructure downtime.

AutoML Similar to the elucidation of data analysis and visualization that Tableau and PowerBI provided, AutoML seeks to expand the capabilities of machine learning beyond those of practicing data scientists. While broad enterprise AutoML platforms such as Datarobot and Dataiqu have grown in recent years, companies like Pecan, BeyondMinds, Noogata, and others are developing AutoML integrations into companies existing analytic workflows, providing powerful use-case and sector specific predictive powers.

Infrastructure Model complexity and scale are rapidly increasing, necessitating faster, cheaper, and more efficient infrastructure. Many frameworks to date are built on combinations of GPUs and traditional storages, mediums ill-equipped for the task. The Israeli MLOps ecosystem has made significant leaps in this arena, with startups such as Habana and Hailo crafting new AI-dedicated chips for data centers, while organizations like Run:AI virtualize existing clusters of GPUs. VAST Data, a portfolio company of Greenfield Partners, and Weka materially increase storage speeds, optimizing data centers to handle the steep requirements of modern AI applications.

While the promise of AI has made its way into our lives, its steep barriers and increasing requirements have made only the most technologically advanced organizations able to harness its true potential. The entrance of MLOps, however, addresses these complexities, lending accessibility to ever-increasing cohorts seeking to leverage AI with less complexity and required expertise.

The article was written by Shay Grinfeld, Managing Partner, and Itay Inbar, Senior Associate, at Greenfield Partners.

See the article here:

MLOps as the key to unlocking the potential of AI | Ctech - CTech

A report recently published by Politico about the automated transcription service Otter.ai serves as a great reminder of how difficult it can be to keep things truly private in the age of cloud-based services. It starts off with a nerve-wracking story the journalist interviewed Mustafa Aksu, a Uyghur human rights activist who could be a target of surveillance from the Chinese government. But though they took pains to keep their communication confidential, they used Otter to record the call and a day later, they received a message from Otter asking about the purpose of the conversation with Aksu.

Obviously, it was a concerning email. After receiving mixed messages from an Otter support agent about whether the survey was real or not, the reporter went down a rabbit hole trying to figure out what had happened. He details his dive into the services privacy policy (which does let Otter share some info with third parties), and lays out how the ease and utility of transcription software can override critical thinking about where potentially sensitive data is ending up.

Its an important wake up call automated transcription services are popping up everywhere, both from standalone companies like Otter (which we at The Verge have used and recommended) and Trint, and as built-in components of services like Zoom and Google Docs. Rationally, we know that the government can get at data stored by these cloud services with a subpoena, but convenience and accessibility can sometimes make it easy to forget those concerns. As the report says, though:

We have not and would not share any data, including data files, of yours with any foreign government or law enforcement agencies, Otters Public Relations Manager, Mitchell Woodrow, told me via email. To be clear, unless we are legally compelled to do so by a valid United States legal subpoena, we will not ever share any of your data, including data files, with any foreign government or law enforcement agencies.

The report is more of a wake up call than a takedown of a popular service theres no big reveal that the transcript had been accessed by a nations spy agency, and Otter told the reporter that Aksus name was in the survey because it was in the title of the transcription. The company also said that its stopped doing those kinds of surveys, because of the disconcerting effect they could have.

But the fact that the government can legally get its hands on the information we provide to these services is something worth keeping in mind especially when it comes to choosing between cloud services and alternatives like apps that use on-device transcription, or offline recorders. Even for those of us not dealing with confidential sources, its well worth reading a report about these increasingly common transcription tools from someone who does.

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This journalist's Otter.ai scare is a reminder that cloud transcription isn't completely private - The Verge