Category Archives: Ai

If you look at a photograph of leopards, would you be able to tell which two were related based on their spots?

Unless youre a leopard expert, the answer is most likely not, says Tanya Berger-Wolf, director of the Translational Data Analytics Institute (TDAI) at Ohio State University. But, she says, computers can.

Berger-Wolf and her team are pioneering a new field of study called imageomics. As the name suggests, imageomics uses machine learning to extract biological data from photos and videos of living organisms. Berger-Wolf and her team have recently begun collaborating with researchers studying leopards in India to compare spot patterns of moms and children using algorithms.

Images have become the most abundant source of information now, and we have the technology, too. We have computer vision machine learning, says Berger-Wolf. She compares this technology to the invention of the microscope, offering scientists a completely different way to look at wildlife.

Building on TDAIs open-source platform called Wildbook, which helps wildlife researchers gather and analyze photos, the team is now focusing on generative AI approaches. These programs use existing content to generate meaningful data. In this case, they are attempting to analyze crowdsourced images to make biological traits that humans may naturally miss computable, like the curvature of a fishs fin or a leopards spots. The algorithms scan images of leopards publicly available online, from social media to digitized museum collections.

In simple terms, the algorithms quantify the similarity, she says. The aim is to help wildlife researchers overcome a data deficiency problem and, ultimately, better protect animals at risk of extinction.

Ecologists and other wildlife researchers are currently facing a data crunch its tedious, expensive, and time-consuming for people to spend time in the field monitoring animals. Due to these challenges, 20,054 species on the International Union for Conservation of Natures (IUCN) Red List of Threatened Species are labeled as data deficient, meaning theres not enough information to make a proper assessment of its risk of extinction. As Berger-Wolf sums it up, biologists are making decisions without having good data on what were losing and how fast.

The platform started with supervised learning Berger-Wolf says the computer uses algorithms simpler than Siri to count how many animals are in the image, as well as where it was taken and when, which could contribute to metrics like population counts. Not only can AI do this at a much lower cost than hiring people but also at a faster rate. In August 2021, the platform analyzed 17 million images automatically.

There are also barriers that only a computer can seem to overcome. Humans are not the best ones at figuring out whats the informative aspect, she says, noting how humans are biased in how we see nature, focusing mostly on facial features. Instead, AI can scan for features humans would likely miss, like the color range of the wings on a tiger moth. A March 2022 study found that the human eye couldnt tell male polymorphic wood tiger moth genotypes apart but moth vision models with ultraviolet light sensitivity could.

Thats where all the true innovation in all of this is, Berger-Wolf says. The team is implementing algorithms that create pixel values of patterned animals, like leopards, zebras, and whale sharks, and analyze those hot spots where the pixel values change most its like comparing fingerprints. Having these fingerprints means researchers can track animals non-invasively and without GPS collars, count them to estimate population sizes, understand migration patterns, and more.

As Berger-Wolf points out, population size is the most basic metric of a species well-being. The platform scanned 11,000 images of whale sharks to create hot spots and help researchers identify individual whale sharks and track their movement, which led to updated information about their population size. This new data pushed the IUCN to change the conservation status of the whale shark from vulnerable to endangered in 2016.

There are also algorithms using facial recognition for primates and cats, shown to be about 90 percent accurate, compared to humans being about 42 percent accurate.

Generative AI is still a burgeoning field when it comes to wildlife conservation, but Berger-Wolf is hopeful. For now, the team is cleaning the preliminary data of the leopard hot spots to ensure the results are not data artifacts or flawed and are true biologically meaningful information. If meaningful, the data could teach researchers how species are responding to changing habitats and climates and show us where humans can step in to help.

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Wildlife photos are a new treasure trove for AI-driven conservation research - The Verge

Leading AI company hosts 400+ global energy, manufacturing, and government leaders at HyperWerx, its 50-acre proving ground, which brings the physical world together with AI.

AUSTIN, Texas, April 21, 2022 /PRNewswire/ -- SparkCognition, a global leader in artificial intelligence (AI) software solutions perfected for business, will host Time Machine Interactive: AI in the Physical World (TMI22) at their 50-acre AI proving ground, HyperWerx today. TMI22 brings over 400 executives across critical industries such as oil and gas, renewables, manufacturing, national security, and defense to the greater Austin area. Guests will experience AI-enabled interconnected and intelligent physical systems, which include examples of IoT, autonomous flight, augmented reality, and cybersecurity. TMI22 is presented by SparkCognition, Gold Sponsor DLA Piper, Bronze Sponsor Raytheon Technologies, SkyGrid, and SparkCognition Government Systems (SGS).

"In the face of climate change and net-zero initiatives, aging and failing assets, emerging cyberthreats to IT and OT infrastructure, an aging workforce and consequential skill gaps, and data overload, AI has become a necessity for every industry," said Stephen Gold, Chief Marketing Officer of SparkCognition. "At TMI22, we are pleased to welcome leading minds from across these sectors to explore tangible, actionable ways in which organizations can tackle their most critical problems and achieve meaningful bottom-line performance."

TMI22 features speakers from major industries, including:

The technology demonstrations at TMI22 include:

To learn more about SparkCognition, visit http://www.sparkcognition.com.

About SparkCognitionSparkCognition's award-winning AI solutions allow organizations to predict future outcomes, optimize processes, and prevent cyberattacks. We partner with the world's industry leaders to analyze, optimize, and learn from data, augment human intelligence, drive profitable growth, and achieve operational excellence. Our patented AI, machine learning, and natural language technologies lead the industry in innovation and accelerate digital transformation. Our solutions allow organizations to solve critical challengesprevent unexpected downtime, maximize asset performance, optimize prices, and ensure worker safety while avoiding zero-day cyberattacks on essential IT and OT infrastructure. To learn more about how SparkCognition's AI solutions can unlock the power in your data, visit http://www.sparkcognition.com.

SparkCognition Contact InfoCara SchwartzkopfCommunications Manager[emailprotected]251-501-6121

SOURCE SparkCognition

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SparkCognition Hosts World Leaders to Show the Future of AI in Business at Time Machine Interactive Event - PR Newswire

Advertising, particularly online advertising, isnt a surefire way to bolster business. A report from ecommerce analytics platform Glew drives the point home: In 2015, 75% of retailers that spent at least $5,000 on Facebook ads ended up losing money on those ads, with the average return on investment landing around -66.7%. Obviously, thats just one segment retail. But the picture doesnt brighten even after broadening out to all categories of advertising. A 2018 survey of marketers by Rakuten Marketing found that companies waste an estimated 26% of their budgets on inefficient ad channels and strategies.

Jaleh Rezaei, the CEO of Mutiny, believes that the problem doesnt lie with the ads themselves. Rather, she pegs it on static, templated websites that dont match the personalization delivered by ads. When buyers follow on an ad online, they often land on a generic website without a targeted call to action, and soon leave not understanding why they should buy.

I faced the conversion problem firsthand when I ran marketing at Gusto, Rezaei told TechCrunch via email. We were successfully driving top-of-funnel growth through ads and other channels, but it wasnt converting into revenue. We solved this problem by creating a growth engineering team that wrote a lot of custom code to drive customers to buy from optimizing our website and signup form to driving upsell and referrals in-app. But most companies dont have the engineers or know-how to do all that.

That, Rezaei says, is why she co-founded Mutiny, which today announced that it raised $50 million in a Series B round co-led by Tiger Global and Insight Partners at a $600 million valuation.Mutinys platform is designed to plug into a companys data and website, using AI to serve thousands of versions of the site to different users.

Growing revenue is the number one priority of every CEO and C-level executive. Over the past decade, companies like Google, Facebook and LinkedIn, along with an ecosystem of adtech and SEO tooling, have made it easy for companies to get in front of their target buyers online, Rezaei continued. However, now that spending money online has become table stakes, the puck has shifted to focusing on reducing marketing waste and turning those dollars into revenue.

Prior to co-founding San Francisco-based, Y Combinator-backed Mutiny, Rezaei was the director of product marketing at VMware. She went onto join the marketing team at Gusto, a payroll management platform, before serving in advisory roles at Y Combinator and Google.

Mutinys other co-founder, Nikhil Mathew, helped to launch LiveGit, an online tool for real-time music collaboration. He then went on to become a head software engineer at Gusto, where he managed and lead the developer infrastructure team. (Rezaei and Mathew worked together while at Gusto.)

An example of website copy generated by Mutiny.

The idea behind Mutiny was to develop an AI system that can learn from a companys online data to provide guidance on underperforming customer segments, Rezaei says. Specifically, Mutiny recommends segments for personalization and shows companies how others personalized for that segment. It might suggest to an enterprise company, for example, that small startups dont convert well on their website, and then show them how rivals personalized their homepages.

In marketing parlance, convert refers to a visitor completing a desired goal, whether thats purchasing a product or simply volunteering their contact information.

Today, companies can use a longtail of manually-intensive alternatives such as connecting data to A/B testing tools, creating hundreds of landing pages, or hiring growth engineers and data scientists to manually connect and analyze data, ideate and custom build solutions for different customer segments, and measure and iterate in-house, Rezaei said. There are also point solutions that help companies with various aspects of personalization, but they either dont use AI or theyre a managed service that the customer cannot leverage self-serve We are creating a new category that makes it easy for any marketer to create personalized experiences and increase conversion.

Mutiny whose AI also learns from different customers site data can generate copy for a website based on what has worked for another, adjacent brands audience. (Rezaei claims that the data is fully anonymized, never shared nor sold, and compliant with relevant privacy laws.) Via AI startup OpenAIs API, Mutiny taps GPT-3, an AI system that can generate convincingly human-sounding text. While Mutiny initially applied GPT-3 only to site headline suggestions, the company eventually began applying the model to whole-page generation, leveraging Mutinys customer data to tune GPT-3 for the purpose.

Our AI is learning from a proprietary data set of hundreds of standardized, anonymized buyer attributes and the content that leads them to convert. We layer this on top of text data from GPT-3 to generate high-converting website copy [Copy is generated for] segments based on user-selected value props like security and ease of use' Rezaei explained. We also train reinforcement learning algorithms with 150 million data points to predict what content resonates best with each individual buyer.

Mutiny competes with several rivals in the AI-powered website personalization space, including Intellimize and Constructor. But the company has impressive momentum behind it. Mutinys customers include Dropbox, Snowflake, Qualtrics and Carta, and Rezaei claims that roughly 50 million people across more than 3 million companies have seen a website personalized by the platforms AI engine. Revenue is on track to quadruple in fiscal quarter 2022.

Rezaei believes that the skills gap in marketing will be one driver of future growth. That remains to be seen a 2021 Clevertouch Marketing survey found that 72% of companies view marketing talent as more essential than technology. But Rezaei makes the case that few companies, particularly in the startup space, have a competency around converting spend into revenue.

The pandemic has forced most commerce and purchasing online for every type of companies. This is compounded by the funding market where companies are raising mega-rounds, the majority of which is earmarked for rapid growth, Rezaei said. As a result, we are seeing online customer acquisition become a board-level concern even for business-to-business companies with large sales teams. Most companies can hire the talent and access the technology needed to advertise, distribute content and raise awareness online [But] companies [dont] have command of efficient online spend.

Another webpage generated by Mutinys AI engine.

Another burden on Mutiny will be convincing potential customers that its platform can overcome the common limitations of personalization engines. As Paul Roetzer writes for Marketing AI Institute, AI without rich data sets can quickly fall short of accuracy benchmarks especially when executives have unrealistic expectations.

We have invested heavily in our AI engine since our Series A, Rezaei said. The result is a fully guided experience for marketers to drive revenue faster based on whats working for their different buyer segments.

Sequoia Capital, Cowboy Ventures and Uncork Capital also invested in Mutinys Series B, joined by executives from Uber, Visa, Salesforce, Square, Figma, Cond Nast, Carta, Snowflake and Atlassian. The company, whose total capital raised stands at $72 million, plans to more than double the size of its 40-person team by 2023 while invest[ing] heavily in its AI technology.

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Mutiny, which personalizes website copy and headlines using AI, raises $50M - TechCrunch

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column aims to collect some of the most relevant recent discoveries and papers particularly in, but not limited to, artificial intelligence and explain why they matter.

This week AI applications have been found in several unexpected niches due to its ability to sort through large amounts of data, or alternatively make sensible predictions based on limited evidence.

Weve seen machine learning models taking on big datasets in biotech and finance, but researchers at ETH Zurich and LMU Munich are applying similar techniques to the data generated by international development aid projects such as disaster relief and housing. The team trained its model on millions of projects (amounting to $2.8 trillion in funding) from the last 20 years, an enormous dataset that is too complex to be manually analyzed in detail.

You can think of the process as an attempt to read an entire library and sort similar books into topic-specific shelves. Our algorithm takes into account 200 different dimensions to determine how similar these 3.2 million projects are to each other an impossible workload for a human being, said study author Malte Toetzke.

Very top-level trends suggest that spending on inclusion and diversity has increased, while climate spending has, surprisingly, decreased in the last few years. You can examine the dataset and trends they analyzed here.

Another area few people think about is the large number of machine parts and components that are produced by various industries at an enormous clip. Some can be reused, some recycled, others must be disposed of responsibly but there are too many for human specialists to go through. German R&D outfit Fraunhofer has developed a machine learning model for identifying parts so they can be put to use instead of heading to the scrap yard.

Image Credits: Fraunhofer

The system relies on more than ordinary camera views, since parts may look similar but be very different, or be identical mechanically but differ visually due to rust or wear. So each part is also weighed and scanned by 3D cameras, and metadata like origin is also included. The model then suggests what it thinks the part is so the human inspecting it doesnt have to start from scratch. Its hoped that tens of thousands of parts will soon be saved, and the processing of millions accelerated, by using this AI-assisted identification method.

Physicists have found an interesting way to bring MLs qualities to bear on a centuries-old problem. Essentially researchers are always looking for ways to show that the equations that govern fluid dynamics (some of which, like Eulers, date to the 18th century) are incomplete that they break at certain extreme values. Using traditional computational techniques this is difficult to do, though not impossible. But researchers at CIT and Hang Seng University in Hong Kong propose a new deep learning method to isolate likely instances of fluid dynamics singularities, while others are applying the technique in other ways to the field. This Quanta article explains this interesting development quite well.

Another centuries-old concept getting an ML layer is kirigami, the art of paper-cutting that many will be familiar with in the context of creating paper snowflakes. The technique goes back centuries in Japan and China in particular, and can produce remarkably complex and flexible structures. Researchers at Argonne National Labs took inspiration from the concept to theorize a 2D material that can retain electronics at microscopic scale but also flex easily.

The team had been doing tens of thousands of experiments with one-six cuts manually, and used that data to train the model. They then used a Department of Energy supercomputer to perform simulations down to the molecular level. In seconds it produced a 10-cut variation with 40% stretchability, far beyond what the team had expected or even tried on their own.

Image Credits: Argonne National Labs

It has figured out things we never told it to figure out. It learned something the way a human learns and used its knowledge to do something different, said project lead Pankaj Rajak. The success has spurred them to increase the complexity and scope of the simulation.

Another interesting extrapolation done by a specially trained AI has a computer vision model reconstructing color data from infrared inputs. Normally a camera capturing IR wouldnt know anything about what color an object was in the visible spectrum. But this experiment found correlations between certain IR bands and visible ones, and created a model to convert images of human faces captured in IR into ones that approximate the visible spectrum.

Its still just a proof of concept, but such spectrum flexibility could be a useful tool in science and photography.

Meanwhile, a new study co-authored by Google AI lead Jeff Dean pushes back against the notion that AI is an environmentally costly endeavor, owing to its high compute requirements. While some research has found that training a large model like OpenAIs GPT-3 can generate carbon dioxide emissions equivalent to that of a small neighborhood, the Google-affiliated study contends that following best practices can reduce machine learning carbon emissions up to 1000x.

The practices in question concern the types of models used, the machines used to train models, mechanization (e.g. computing in the cloud versus on local computers) and map (picking data center locations with the cleanest energy). According to the coauthors, selecting efficient models alone can reduce computation by factors of five to 10, while using processors optimized for machine learning training, such as GPUs, can improve the performance-per-Watt ratio by factors of two to 5.

Any thread of research suggesting that AIs environmental impact can be lessened is cause for celebration, indeed. But it must be pointed out that Google isnt a neutral party. Many of the companys products, from Google Maps to Google Search, rely on models that required large amounts of energy to develop and run.

Mike Cook, a member of the Knives and Paintbrushes open research group, points out that even if the studys estimates are accurate there simply isnt a good reason for a company not to scale up in an energy-inefficient way if it benefits them. While academic groups might pay attention to metrics like carbon impact, companies arent as incentivized in the same way at least currently.

The whole reason were having this conversation to begin with is that companies like Google and OpenAI had effectively infinite funding, and chose to leverage it to build models like GPT-3 and BERT at any cost, because they knew it gave them an advantage, Cook told TechCrunch via email. Overall, I think the paper says some nice stuff and its great if were thinking about efficiency, but the issue isnt a technical one in my opinion we know for a fact that these companies will go big when they need to, they wont restrain themselves, so saying this is now solved forever just feels like an empty line.

The last topic for this week isnt actually about machine learning exactly, but rather what might be a way forward in simulating the brain in a more direct way. EPFL bioinformatics researchers created a mathematical model for creating tons of unique but accurate simulated neurons that could eventually be used to build digital twins of neuroanatomy.

The findings are already enabling Blue Brain to build biologically detailed reconstructions and simulations of the mouse brain, by computationally reconstructing brain regions for simulations which replicate the anatomical properties of neuronal morphologies and include region specific anatomy, said researcher Lida Kanari.

Dont expect sim-brains to make for better AIs this is very much in pursuit of advances in neuroscience but perhaps the insights from simulated neuronal networks may lead to fundamental improvements to the understanding of the processes AI seeks to imitate digitally.

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Deep Science: AI cuts, flows and goes green - TechCrunch

Children and young people are growing up in an increasingly digital age, where technology pervades every aspect of their lives. From robotic toys and social media to the classroom and home, artificial intelligence (AI) is a ubiquitous part of daily life. It's vital therefore that ethical guidelines protect them and ensure they get the best from this emerging technology.

Generation Z, who have grown up with AI, are uniquely placed to offer an insight into the potential issues of AI targeted at children and help create governance guidelines. With that in mind the World Economic Forum has set up the AI Youth Council, a global diverse group comprising young people interested in AI.

Members serve as part of the Generation AI project community and have been central to the creation of the Artificial Intelligence for Children Toolkit, published 29 March 2022. The AI Youth Council is designed to bring together young people from 14 to 21 years of age worldwide to discuss AI ethics and governance.

Born between 1996 and 2012, Generation Z is recognized as the digital native generation. We were raised in an era defined by the internet, a time characterized by massive digitalization: social networks were launched, new technologies were created, and AI began its cross-industry debut.

As a result, Gen Z evolved alongside technology, which impacted our childhood in multiple dimensions. With social media, our methods of interaction changed. Instant connectivity translated to spending time with friends 24/7. We easily absorbed new tech trends, and our education was augmented by the integration of new software.

Similarly, born between 2013-2024, Generation Alpha, the first true AI native generation, is experiencing the effects of AI right now. Kids seamlessly interact with AI chatbots and smart toys, use of IoT devices is second nature, and they are used to real-time information access. The effects of AI on childhood are evident: it makes kids crave optimized experiences and hyper-connectivity, whether at home, in school or with friends.

Jianyu Gao, Columbia University, BS in Computer Science, USA

I was raised on an unregulated internet with minimal literacy in privacy and safety, and the adults around me didnt know how to educate me to protect myself because they were just as ignorant as I was. I did stay out of danger because I knew what I was doing I was lucky, but too many other children were not. The internet has given us the opportunity to connect with people around the world who would otherwise be out of reach, but has also exposed children to disturbing content, harmful ideologies, brainwashing communities and social circles, cyberbullies and online stalkers, predators, or other dangerous elements who might not have had access to them in real life.

As we transition into a post-pandemic world that not only lives with the internet but lives on the internet, I reflect on my childhood as a girl with a laptop with worries for the future, but also with the resolve to do better for the youth that will grow up with AI. If we expect AI to be just as human as we are, then we must learn from my generations experiences growing up with technologies such as the internet and prepare for the prospect that AI will not always learn from the best of us.

Guido Putignano, Bachelor of Engineering and Biomedical Engineering, Politecnico di Milano

One of the first times I got in contact with technology was when I was 12. I went to the shop and I saw strange objects that could make you go into other dimensions. At that time, these devices were a one-hour distraction after my evening homework. From the beginning, I remember that technology being harmful to me. I wasted many days watching videos without being intentional about what I was learning. When I turned 16, I started to use these devices proactively. I started making these devices work for me, rather than the opposite.

I think that the best way to predict the future is to create it yourself. I am an optimist, and I think that personalisation and high-speed connectivity will make anything far better than it is today. In this case, AI systems go from being objects to being subjects. One example is in the fields of education and healthcare. Imagine how awesome it would be to have an AI system that could help you personalise many parts of your life and be at the centre of your growth. That system could track thousands of parameters, making astonishingly accurate predictions of your future self. Those opportunities will be a reality in the future.

Joy Fakude, University of Johannesburg, South Africa

Personally, growing up in South Africa, technology didnt have that great of an impact in my life because I didnt have access to it. The closest I came to a laptop was a toy laptop where I practised maths questions, English sentence construction and played some games. Then I advanced to my first cell phone a BlackBerry which I thought was the coolest thing on planet Earth. I then had access to the internet and social media, however access to WiFi became a serious problem and unfortunately that is the reality for a lot of South African youngsters today.

Not having access to data never mind smart phones or laptops in a world that is speeding into a digital era, many South African teens are left behind not even knowing what AI is or what a digital footprint is, or not even knowing how to protect your data. My biggest concern for future generations of South Africans is that the rapid developments of AI technologies leave them stuck in the mud. That they arent taught and because they arent taught cant adapt and, according to Darwins theory of evolution, dont survive.

Born in 2003, my childhood was situated in the transitional stage from floppy disks and BlackBerry phones to social media powerhouses and streaming services. Most of my early interaction with technology was limited to my Sony camera and Nintendo S4. By the time I was 11, I relented to peer pressure and created an Instagram account. As I pondered how to use my new platform, it seemed natural to present the version of myself that fit my current interests. I used the profile Gracie Dancer to perform self-choreographed dance routines or rave about my new tap shoes. Gracie Singer was where I posted all my off-pitched covers of the latest pop songs.

But what was on the surface an apparently innocuous search for a sense of community began affecting me in a way I didnt expect. As my interests evolved, I felt I was wrong for wanting to try different things. The uncertainty of not knowing who there was behind the screen made me feel as if though I was constantly being watched and judged. I began to fear mistakes at a time in my life when they should have been the most welcomed.

While technology has undeniable potential, I worry that the coming generation of children are growing up in a society where we are understood by others solely through our internet personas. Genuine relationships, interests, and activities will come second to keeping up the illusion of perfection, which so often means conformity.

Ecem Yilmazhaliloglu from Turkey, studying at Stanford University

As the last generation to learn navigating bulky, old system units in computer lessons at school and the first generation to grow up having a Facebook profile, I belong to what Id like to call the transition generation. As the new technologies social media, touch screens, cloud storage systems, and AI rapidly made their way into our world and our homes, we learned to adapt and experiment though trial and error, as there was no previous generation to show us the ropes.

When I got my first computer and opened up my first social media profile at eight, to play online games with my school friends, none of us thought about the consequences of our actions. Starting to engage in these technologies with the purest of intentions, in our attempt to fulfill the basic human need to socialize, to connect, we made ourselves vulnerable to the dangers that lurked in the technologys shadows. The world has since realized its mistake in being unprepared against these dangers, but many of us had already become victims of catfishing, hacking, and even stalking.

While technology offered us many benefits there was always equal amounts of risk involved. Having learnt this in the past decade, both adults and children, it is our responsibility to provide guidance, protect the next generations and help navigate these technologies responsibly and for the good of all. It is our job to take action on minimizing the risks and maximizing the benefits, and provide the necessary wisdom and support, which we, the transition generation, lacked.

Kathleen Esfahany, Computer Science & Neurology, MIT, USA

I was born in 2000 to two computer scientists. Although technological innovation dramatically changed my life with each passing year, the shift into a technology-filled world felt entirely natural to me. The phenomenon of mirrored growth helped cement my identity as a digital native: for much of my childhood, each milestone in my own cognitive development was mirrored by technological advances and a deepening immersion in technology as an educational and social tool.

As my curiosity about the world grew, online news and social media proliferated, making it possible for me to follow events and connect with others across the globe. I can also thank my parents, who used their expertise on computers to help me understand the seemingly magical devices around me, empowering me to think about how to use technology for my own purposes and create technology of my own.

Todays youth are growing up with rapid advances in AI. Already, we are seeing how the unprecedented efficiency and personalization of AI-powered technology can elevate todays youths ability to learn, form personal relationships, and create joy. To optimize it for childrens safety and emotional well-being, I believe it is critical that AI-powered technology is designed so that it can match the diverse needs and abilities found throughout childhood development. My hope is that the combination of well designed AI-powered technology for youth and educational programs about AI will empower and inspire todays youth to harness AI responsibly to bring to life their visions for the future.

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AI ethics: digital natives on protecting future generations | World Economic Forum - World Economic Forum

EDMONTON, Alberta, April 14, 2022 (GLOBE NEWSWIRE) -- Amii (the Alberta Machine Intelligence Institute) has announced the program for AI Week, May 24-27 in Edmonton, Canada. With more than 20 events taking place across four days throughout the city, the celebration of Albertas AI excellence will feature an academic keynote from Richard S. Sutton, leading expert in reinforcement learning, who will discuss future research directions in the field.

The jam-packed week also includes panels on AI career paths for kids, AI for competitive advantage and the ethics of AI; a career and talent mixer connecting AI career seekers with top companies; and a full-day academic symposium bringing together the brightest minds in AI. The celebrations are rounded out by a house party at a secret, soon-to-be-revealed location and the Amiiversary street party, marking 20 years of AI excellence in Alberta. Learn more about the program at http://www.ai-week.ca/program

Over the past 20 years, Alberta has emerged as one of the worlds top destinations for AI research and application, says Cam Linke, CEO of Amii. With AI Week, were putting a global spotlight on the province and welcoming the worlds AI community to experience what many in the field have known for a long time: that Alberta is at the forefront of the AI revolution. AI Week isnt just a celebration of 20 years of AI excellence its a launching point for the next 20 years of advancement.

AI Week has something for everyone including sessions, networking events and socials for a range of ages and familiarity with AI. Additional keynotes will be delivered by Alona Fyshe, speaking about what brains and AI can tell us about one another, and Martha White, who will present on innovative applications of reinforcement learning. A special AI in Health keynote will highlight the work of Dornoosh Zonoobi and Jacob Jaremko of Medo.ai, which uses machine learning in concert with ultrasound technology to screen infants for hip dysplasia.

Informal networking and social events will help forge connections between members of the research, industry and innovation communities as well as AI beginners and enthusiasts. Meanwhile, the Amiiversary street party, hosted on Rice Howard Way in Edmontons downtown core, will mark 20 years of AI excellence in Alberta. The party will be attended by the whos-who of Edmonton AI, technology and innovation scenes.

AI Week will be attended by the worlds AI community, with over 500 applicants for travel bursaries from more than 35 different countries. The successful applicants, emerging researchers and industry professionals alike, will have the opportunity to learn alongside leaders in the field at the AI Week Academic Symposium, which is being organized by Amiis Fellows from the University of Alberta, one of the worlds top academic institutions for AI research. The symposium will include talks and discussions among top experts in AI and machine learning as well as demos and lab showcases from the Amii community.

I chose to set up in Canada in 2003 because, at the time, Alberta was one of the few places investing in building a community of AI researchers, says Richard S. Sutton, Amiis Chief Scientific Advisor, who is also a Professor at the University of Alberta and a Distinguished Research Scientist at DeepMind. Nearly twenty years later, I am struck by how much we have achieved to advance the field of AI, not only locally but globally. AI Week is an opportunity to celebrate those achievements and showcase some of the brightest minds in AI.

The event is being put on by Amii, one of Canadas AI institutes in the Pan-Canadian AI Strategy and will feature event partners and community-led events from across Canadas AI ecosystem. AI Week is made possible in part by our event partners and talent bursary sponsors: AltaML, Applied Pharmaceutical Innovation, ATB, Attabotics, BDC, CBRE, CIFAR, DeepMind, DrugBank, Explore Edmonton, NeuroSoph, RBC Royal Bank, Samdesk, TELUS and the University of Alberta.

About Amii

One of Canadas three centres of AI excellence as part of the Pan-Canadian AI Strategy, Amii (the Alberta Machine Intelligence Institute) is an Alberta-based non-profit institute that supports world-leading research in artificial intelligence and machine learning and translates scientific advancement into industry adoption. Amii grows AI capabilities through advancing leading-edge research, delivering exceptional educational offerings and providing business advice all with the goal of building in-house AI capabilities. For more information, visit amii.ca.

Spencer MurrayCommunications & Public Relationst: 587.415.6100 ext. 109 | c: 780.991.7136spencer.murray@amii.ca

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AI Week brings together the world AI community - GlobeNewswire

Artificial intelligence (AI) is now capable of creating plausible images of people, thanks to websites such as This person does not exit, as well as non-existent animals, and even rooms for rent.

The similarity between the mocked-up images and reality is such that it has been flagged up by research published in Proceedings of the National Academy of Sciences of the United States of America (PNAS), which compares real faces with those created through an AI algorithm known as generative adversarial networks (GANs).

Not only does the research conclude that it was hard to distinguish between the two, it also shows that fictitious faces generate more trust than real ones. Although the difference is slight at just 7.7%, the faces that were considered least trustworthy were real while three of the four believed to be most trustworthy were fictitious. Sophie Nightingale, Professor of Psychology at Lancaster University and co-author of the study, says the result wasnt something the team were expecting: We were quite surprised, she says.

The research was based on three experiments. In the first experiment, the 315 participants asked to distinguish between real and synthesized faces scored an accuracy rate of 48.2%. In the second experiment, the 219 different participants were given tips on distinguishing synthetic faces from real ones with feedback on their guesses. The percentage of correct answers in the second experiment was slightly higher than in the first, with an accuracy rate of 59%. In both cases, the faces that were most difficult to classify were those of white people. Nightingale believes that this discrepancy is due to the fact that the algorithms are more used to this type of face, but that this will change with time

In the third experiment, the researchers wanted to go further and gauge the level of trust generated by the faces and check whether the synthetic ones triggered the same levels of confidence. To do this, 223 participants had to rate the faces from 1 (very untrustworthy) to 7 (very trustworthy). In the case of the real faces, the average score was 4.48 versus 4.82 for the synthetic faces. Although the difference amounts to just 7.7%, the authors stress that it is significant. Of the 4 most trustworthy faces, three were synthetic, while the four that struck the participants as least trustworthy were real.

All the faces analyzed belonged to a sample of 800 images, half real and half fake. The synthetic half was created by GANs. The sample was composed of an equal number of men and women of four different races: African American, Caucasian, East Asian and South Asian. They matched the actual faces with a synthetic one of a similar age, gender, race and general appearance. Each participant analyzed 128 faces.

Jos Miguel Fernndez Dols, professor of Social Psychology at the Autonomous University of Madrid whose research focuses on facial expression, points out that not all faces have the same expression or posture and that this can affect judgments. The study takes into account the importance of facial expression and assumes that a smiling face is more likely to be rated more trustworthy. However, 65.5% of the real faces and 58.8% of the synthetic faces were smiling, so facial expression alone cannot explain why synthetics are rated as more trustworthy.

The researcher also considers the posture of three of the images rated less trustworthy to be critical; pushing the upper part of the face forward with respect to the mouth and protecting the neck, is a posture that frequently precedes aggression. Synthetic faces are becoming more realistic and can easily generate more trust by playing with several factors: the typical nature of the face, features and posture, says Fernndez Dols.

In addition to the creation of synthetic faces, Nightingale predicts that other types of artificially created content, such as videos and audios, are on their way to becoming indistinguishable from real content. It is the democratization of access to this powerful technology that poses the most significant threat, she says. We also encourage reconsideration of the often-laissez-faire approach to the public and unrestricted releasing of code for anyone to incorporate into any application.

To prevent the proliferation of non-consensual intimate images, fraud and disinformation campaigns, the researchers propose guidelines for the creation and distribution of synthesized images to protect the public from deep fakes.

But Sergio Escalera, professor at the University of Barcelona and member of the Computer Vision Center, highlights the positive aspect of AI generated faces: It is interesting to see how faces can be generated to transmit a friendly emotion, he says, suggesting this could be incorporated into the creation of virtual assistants or used when a specific expression needs to be conveyed, such as calm, to people suffering from a mental illness.

According to Escalera, from an ethical point of view, it is important to expose AIs potential and, above all, to be very aware of the possible risks that may exist when handing that over to society. He also points out that current legislation is a little behind technological progress and there is still much to be done.

Continued here:

Faces created by AI are more trustworthy than real ones - EL PAS in English

Martial Hebert, dean of the Carnegie Mellon University School of Computer Science, during a recent visit to the GeekWire offices in Seattle. (GeekWire Photo / Todd Bishop)

This week on the GeekWire Podcast, we explore the state of the art in robotics and artificial intelligence with Martial Hebert, dean of the Carnegie Mellon University School of Computer Science in Pittsburgh.

A veteran computer scientist in the field of computer vision, Hebert is the former director of CMUs prestigious Robotics Institute. A native of France, he also had the distinguished honor of being our first in-person podcast guest in two years, visiting the GeekWire offices during his recent trip to the Seattle area.

As youll hear, our discussion doubled as a preview of a trip that GeekWires news team will soon be making to Pittsburgh, revisiting the city that hosted our temporary GeekWire HQ2 in 2018, and reporting from the Cascadia Connect Robotics, Automation & AI conference, with coverage supported by Cascadia Capital.

Continue reading for excerpts from the conversation, edited for clarity and length.

Listen below, or subscribe to GeekWire in Apple Podcasts, Google Podcasts, Spotify or wherever you listen.

Why are you here in Seattle? Can you tell us a little bit about what youre doing on this West Coast trip?

Martial Hebert: We collaborate with a number of partners and a number of industry partners. And so this is the purpose of this trip: to establish those collaborations and reinforce those collaborations on various topics around AI and robotics.

It has been four years since GeekWire has been in Pittsburgh. What has changed in computer science and the technology scene?

The self-driving companies Aurora and Argo AI are expanding quickly and successfully. The whole network and ecosystem of robotics companies is also expanding quickly.

But in addition to the expansion, theres also a greater sense of community. This is something that has existed in the Bay Area and in the Boston area for a number of years. What has changed over the past four years is that our community, through organizations like the Pittsburgh Robotics Network, has solidified a lot.

Are self-driving cars still one of the most promising applications of computer vision and autonomous systems?

Its one very visible and potentially very impactful application in terms peoples lives: transportation, transit, and so forth. But there are other applications that are not as visible that can be also quite impactful.

For example, things that revolve around health, and how to use health signals from various sensors those have profound implications, potentially. If you can have a small change in peoples habits, that can make a tremendous change in the overall health of the population, and the economy.

What are some of the cutting-edge advances youre seeing today in robotics and computer vision?

Let me give you an idea of some of the themes that I think are very interesting and promising.

It seems like that first theme of sensing, understanding and predicting human behavior could be applicable in the classroom, in terms of systems to sense how students are interacting and engaging. How much of that is happening in the technology that were seeing these days?

Theres two answers to that:

But what is important is that its not just AI. Its not just computer vision. Its technology plus the learning sciences. And its critical that the two are combined. Anything that tries to use this kind of computer vision, for example, in a naive way, can be actually disastrous. So its very important that that those disciplines are linked properly.

I can imagine thats true across a variety of initiatives, in a bunch of different fields. In the past, computer scientists, roboticists, people in artificial intelligence might have tried to develop things in a vacuum without people who are subject matter experts. And thats changed.

In fact, thats an evolution that I think is very interesting and necessary. So for example, we have a large activity with [CMUs Heinz College of Information Systems and Public Policy] in understanding how AI can be used in public policy. What you really want is to extract general principles and tools to do AI for public policy, and that, in turn, converts into a curriculum and educational offering at the intersection of the two.

Its important that we make clear the limitations of AI. And I think theres not enough of that, actually. Its important even for those who are not AI experts, who do not necessarily know the technical details of AI, to understand what AI can do, but also, importantly, what it cannot do.

[After we recorded this episode, CMU announced a new cross-disciplinary Responsible AI Initiative involving the Heinz College and the School of Computer Science.]

If you were just getting started in computer vision, and robotics, is there a particular challenge or problem that you just couldnt wait to take on in the field?

A major challenge is to have truly comprehensive and principled approaches to characterizing the performance of AI and machine learning systems, and evaluating this performance, predicting this performance.

When you look at a classical engineered system whether its a car or an elevator or something else behind that system theres a couple of hundred years of engineering practice. That means formal methods formal mathematical methods, formal statistical methods but also best practices for testing and evaluation. We dont have that for AI and ML, at least not to that extent.

Thats basically this idea of going from the components of the system, all the way to being able to have characterization of the entire end-to-end system. So thats a very large challenge.

I thought you were going to say, a robot that could get you a beer while youre watching the Steelers game.

This goes to what I said earlier about the limitations. We still dont have the support to handle those components in terms of characterization. So thats where Im coming from. I think thats critical to get to the stage where you can have the beer delivery robot be truly reliable and trustworthy.

See Martial Heberts research page for more details on his work in computer vision and autonomous systems.

Edited and produced by Curt Milton, with music by Daniel L.K. Caldwell.

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The new frontiers of AI and robotics, with CMU computer science dean Martial Hebert - GeekWire

The last year was a busy one for Russias military and civilian artificial intelligence efforts. Moscow poured money into research and development, and Russias civil society debated the countrys place in the larger AI ecosystem. But Vladimir Putins invasion of Ukraine in February and the resulting sanctions have brought several of those efforts to a haltand thrown into question just how many of its AI advancements Russia will be able to salvage and continue.

Ever since Putin extolled the development of robotic combat systems in the new State Armaments Program in 2020, the Russian Ministry of Defense has been hyper-focused on AI. We have learned more about the Russian militarys focus on AI in the past year thanks to several public revelations.

But talk of AI has been muted since the Russian invasion of Ukraine. Apart from the widespread use of UAVs for reconnaissance and target acquisition and a single display of a mine-clearing robotall of which are remote-controlledthere is no overt evidence of Russian AI in C4ISR or decision-making among the Russian military forces, other than a single public deepfake attempt to discredit the Ukrainian government. That does not mean AI isnt used, considering how Ukrainians are now utilizing artificial intelligence in data analysisbut there is a notable absence of larger discussion about this technology in open-source Russian media.

The gap between Russian military aspirations for high-tech warfare of the future and the actual conduct of war today is becoming clear. In January 2021, Colonel-General Vladimir Zarudnitsky, the head of the Military Academy of the Russian Armed Forces General Staff, wrote that the development and use of unmanned and autonomous military systems, the robotization of all spheres of armed conflict, and the development of AI for robotics will have the greatest medium-term effect on the Russian armed forces ability to meet their future challenges. Other MOD military experts also debated the impact of these emerging technologies on the Russian military and future balance of forces. Russia continued to upgrade and replace Soviet-made systems, part of the MODs drive from digitization (weapons with modern information technologies for C4ISR) to intellectualization (widespread implementation of AI capable of performing human-like creative thinking functions). These and other developments were covered in detail during Russias Army-2021 conference, with AI as a key element in C4ISR at the tactical and strategic levels.

Meanwhile, Russian military developers and researchers worked on multiple AI-enabled robotics projects, including the Marker concept unmanned ground vehicle and its autonomous operation in groups and with UAVs.

Toward the end of 2021, the state agency responsible for exporting Russian military technology even announced plans to offer unmanned aviation, robotics, and high-tech products with artificial intelligence elements to potential customers this year. The agency emphasized the equipment is geared toward defensive, border protection, and counter-terrorism capabilities.

Since the invasion, things have changed. Russias defense-industrial complexespecially military high-tech and AI research and developmentmay be affected by the international sanctions and cascading effects of Russia being cut off from semi-conductor and microprocessor imports.

Throughout 2021, the Russian government was pushing for the adoption of its AI civilian initiatives across the country, such as nationwide hackathons aimed at different age groups with the aim of making artificial intelligence familiar at home, work, and school. The government also pushed for the digital transformation of science and higher education, emphasizing the development of AI, big data, and the internet of things.

Russian academic AI R&D efforts drove predictive analytics; development of chat bots that process text and voice messages and resolve user issues without human intervention; and technologies for working with biometric data. Russias development of facial recognition technology continued apace, with key efforts implemented across Moscow and other large cities. AI as a key image recognition and data analytical tool was used in many medical projects and efforts dealing with large data sets.

Russian government officials noted their countrys efforts in promoting the ethics of artificial intelligence, and expressed confidence in Russias continued participation in this UN-sponsored work. The Russian Council for the Development of the Digital Economy has officially called for a ban on artificial intelligence algorithms that discriminate against people.

Russias Ministry of Economic Development was asked to "create a mechanism for assessing the humanitarian impact of the consequences of the introduction of such [AI] technologies, including in the provision of state and municipal services to citizens," and to prepare a "road map" for effective regulation, use, and implementation. According to the council, citizens should be able to appeal AI decisions digitally, and such a complaint should only be considered by a human. The council also proposed developing legal mechanisms to compensate for damage caused as a result of AI use.

In October, Russias leading information and communications companies adopted the National Code of Ethics in the Field of AI; the code was recommended for all participants in the AI market, including government, business, Russian and foreign developers. Among the basic principles in the code are a human-centered approach to the development of this technology and the safety of working with data.

AI workforce development was spelled out as a key requirement when the government officially unveiled the national AI roadmap in 2019. A 2021 government poll that tried to gauge the level of confidence in the governments AI efforts showed that only about 64 percent of domestic AI specialists were satisfied with the working conditions in Russia.

The survey reflected the microcosm of AI research, development, testing, and evaluation in Russialots of government activity and different efforts that did not automatically translate into a productive ecosystem conducive for developing AI, some major efforts notwithstanding.

Among some of the reasons in 2021 that Russia was lagging behind in the development of artificial intelligence technologies were the personnel shortage and the weakness of the venture capital market. The civilian developer community also noted the low penetration of Russian products into foreign markets, dependence on imports, slow introduction of products into business and government bodies, and a weak connection between AI theory and practice.

Russias likely plans to concentrate on these areas in 2022 were revised or put on hold once Russia invaded Ukraine. The sudden pull-out of major IT and high-tech companies from Russia, coupled with a rapid brain drain of Russias IT workers, and the ever-expanding high-tech sanctions against the Russian state may hobble domestic AI research and development for years to come. While the Russian government is trying to prop up its AI and high-tech industry with subsidies, funding, and legislative support, the impact of the above-mentioned consequences may be too much for the still-growing and evolving Russian AI ecosystem. That does not mean AI research and development will stopon the contrary, many 2021 trends, efforts, and inventions are being implemented into the Russian economy and society in 2022, and there are domestic high-tech companies and public-private partnerships which are trying to fill the void left by the departed global IT majors. But the effects of the invasion will be felt in the AI ecosystem for a long time, especially with so many IT workers leaving the country, either because of the massive impact on the high-tech economy, or because they disagree with the war, or both.

One of the most-felt sanctions aftereffects has been the severing of international cooperation on AI among Russian universities and research instructions, which earlier was enshrined as one of the most important drivers for domestic AI R&D, and reinforced by support from the Kremlin. For most high-tech institutions around the world, the impact of civilian destruction across Ukraine by the Russian military greatly outweighs the need to engage Russia on AI. At the same time, much of the Russian military AI R&D took place in a siloed environmentin many cases behind a classified firewall and without significant public-private cooperationso its hard to estimate just how sanctions will affect Russian military AI efforts.

While many in Russia now look to China as a substitute for departed global commercial relationships and products, its not clear if Beijing could fully replace the software and hardware products and services that left Russian markets at this point.

Recent events may not stop Russian civilians and military experts from discussing how AI influences the conduct of war and peacebut the practical implementation of these deliberations may become increasingly more difficult for a country under global high-tech isolation.

Samuel Bendett is an Adjunct Senior Fellow at the Center for a New American Security and an Adviser at the CNA Corporation.

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Russia's Artificial Intelligence Boom May Not Survive the War - Defense One

Top 100 Fastest-Growing AI Teams

To maintain their competitive advantage, enterprises will need to scale their AI capabilities quickly and effectively. Here are the 100 companies with the fastest-growing AI teams, according to this analysis of data from Vectice.

The report studied the 2,500 largest US companies and found that 965 have an AI team of greater than 20 employees. 541 companies that had an AI team of over 20 employees experienced positive growth in their AI team over the past 3 months.

In terms of the number of new hires, Microsoft led the pack, with nearly 500 AI experts hired over the past 3 months. This represents a 8-fold lead over the second-fastest company, Kaiser Permanente.

Other companies in the top 10 for number of new hires include Optum, CVS Health, USAA, General Motors, Ford Motor Company, Robert Half, Walmart, and Bloomberg.

Overall, the fastest-growing teams were found in the technology and healthcare industries, followed by retail and finance/insurance. This indicates that enterprises across a variety of industries are seeing the value in AI and are investing in the technology. This article zooms into the top 10 companies charging the way, as well as an overview of the companies in the top 100.

Top 10 Fastest-Growing AI Teams By New Hires

New hires in the AI space have been growing at a fast clip, with the top 10 companies adding dozens of new AI experts over the past 3 months.

Fastest growing by overall number of new hires

Fastest growing by overall number of new hires

Source: Top 100 Fastest-Growing AI Teams, Vectice

Number of top companies by percentage in healthcare, finance/insurance, tech or retail and CPG

Source: Top 100 Fastest-Growing AI Teams, Vectice

Companies like Microsoft, Kaiser Permanente, and Optum are leading the way in terms of AI team growth. These companies specifically are in the healthcare, insurance, and technology industries respectively. Their focus on AI team growth indicates that they understand the importance of the technology and are investing in it to maintain their competitive advantage.

In terms of the top 10 fastest growing companies by percentage of growth with initial AI team size of 20-49 over the past three months, Concentrix, GameStop and Moody's Corporation take the top 3 picks. Each reflects the IT and Services, Retail and Financial Services industries, respectively.

Top 10 fastest growing companies in percentage of growth with an initial AI team size of 20-49 over the past 3 months

Top 10 fastest growing companies in percentage of growth with an initial AI team size of 20-49 over ... [+] the past 3 months

Source: Top 100 Fastest-Growing AI Teams, Vectice

In terms of the top 10 fastest growing companies by percentage of growth with initial AI team size of 50-250 over the past three months, ResMed, Northside Hospital and General Mills make up the top three. The trend is that healthcare and retail are two of the biggest industries represented. These industries have larger AI team sizes for a number of factors: they're collecting more data than ever before, and the complexity of the data requires more AI expertise to make sense of it.

Top 10 fastest growing companies in percentage of growth with an initial AI team size of 50-250 over the past 3 months

Top 10 fastest growing companies in percentage of growth with an initial AI team size of 50-250 over ... [+] the past 3 months

Source: Top 100 Fastest-Growing AI Teams, Vectice

When it comes to the top 10 fastest growing companies by percentage of growth with initial AI team size of +250 over the past three months, Microsoft, Robert Half, and Optum take the top three spots. This includes companies from a variety of industries: technology, staffing, and healthcare.

Top 10 fastest growing company in percentage of growth with an initial AI team size of +250 over 3 months

Top 10 fastest growing company in percentage of growth with an initial AI team size of +250 over 3 ... [+] months

Source: Top 100 Fastest-Growing AI Teams, Vectice

The Benefits of Being an Early Adopter, And Why Its Important

The top 100 companies with the fastest-growing AI teams were selected based on their ability to scale their AI capabilities quickly and effectively. These companies are able to maintain a competitive advantage by being early adopters of AI technology. There are several benefits to being an early adopter of AI. This section will serve as a deep-dive into three of these benefits: a faster go-to-market time, improved decision-making, and increased customer loyalty.

Why is Microsoft, Kaiser Permanente, and Google Leading the Way?

The top three companies in terms of number of new AI hires are Microsoft, Kaiser Permanente, and Google. These companies are leading the way in AI because they have the resources to invest in AI, as well as a need to scale their AI capabilities quickly.

Microsoft is a leading provider of enterprise software and services. The company has a long history of investing in new technologies, and AI is no exception. Microsoft has been working on AI projects for many years, and has recently made a number of high-profile acquisitions in the space. For example, the company acquired Nuance for $19.7 billion acquisition, a company that provides speech recognition and conversational AI services. Nuance is particularly known for its deep learning voice transcription service, which is quite popular in the healthcare industry.

Kaiser Permanente is one of the largest healthcare providers in the United States. The company has been an early adopter of AI, and is using the technology to improve patient care. For example, Kaiser Permanente is using AI to identify patients at risk of developing certain diseases. This information is then used to make decisions about how to best treat those patients.

Google is a leading provider of consumer services. The company has also been an early adopter of AI, and is using the technology to improve its products and services, and has also acquired a number of companies in the space. For example, back in 2014, Google acquired DeepMind Technologies for $500 million. DeepMind is a leading provider of artificial intelligence technology.

The Way Forward: What Businesses Can Do To Join The Ranks

As enterprises race to build up their AI capabilities, it's important to remember that success with AI requires more than just a team of experts. Enterprises need to have the right data, the right infrastructure, and the right culture in place to make AI a success.

In terms of data, enterprises need to focus on collecting high-quality data that can be used to train AI models. This data needs to be well-labeled and clean, and it should be collected from a variety of sources.

In terms of infrastructure, enterprises need to have the computing power and storage capacity to support AI initiatives. They also need to have a well-defined governance model in place to ensure that AI is used responsibly.

Finally, in terms of culture, enterprises need to create an environment that is conducive to innovation. This includes promoting a data-driven mindset, encouraging creativity, and giving employees the freedom to experiment.

Only by investing in all of these areas will enterprises be able to build successful AI initiatives.

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Top 100 Fastest-Growing AI Teams: Key Players, Exclusive Insights - Forbes