Monthly Archives: May 2022

MANCHESTER, United Kingdom, May 26, 2022 (GLOBE NEWSWIRE) -- Most businesses (90%) now use or plan to use Artificial Intelligence (AI) and even more (98%) have, or intend to, implement a data strategy. Yet new findings suggest that while the success of each is reliant on the other only one in three (35%) businesses with a data strategy say it includes provisions for Artificial Intelligence (AI). Decision Intelligence company Peak, released its State of AI, 2022 report today, highlighting that many businesses are making investments in both data and AI without connecting the two.

AI is inherently a data technology, and must function as part of an overarching data strategy, yet the majority of respondents in this survey are thinking about the two separately, said Richard Potter, co-founder and CEO of Peak.

The amount of data businesses produce is increasing exponentially and current predictions suggest businesses globally will create, capture, copy and consume 97 zettabytes of data this year alone.1 Implementing a data strategy is essential for defining how to collect, store and put that data to use.

Peaks State of AI report suggests that effectively leveraging their data is a priority for many businesses. One in two (52%) organizations with 100 or more employees now have a Chief Data Officer, and 86% have invested in a data lake or warehouse. Still, two-thirds (65%) of those with a data strategy have not made provisions for AI. Instead, data strategies are most likely to be focused on centralizing data within the business (55%), although establishing measurable goals for the use of data (54%) and managing security risks and compliance (53%) are similarly important goals.

The report also uncovered inconsistencies among leadership teams on data strategy. While 89% of CEOs said their company had a data strategy, only 63% of wider c-suite executives agreed.

This disconnect extends to perceptions of AI as well. Of the respondents in businesses that have or are working towards AI, 55% of CEOs thought the technology was being implemented throughout the business, compared to just 42% of other c-suite leaders.

Our research also reveals that within many companies there is a lack of clarity around the overall AI strategy, even at the top levels of management, said Richard Potter. If businesses want to successfully implement AI and, critically, drive value with this technology, we need open discussion within a business to ensure everyone is aligned to and understands the vision.

The report also provides a look at the regional differences in attitudes and progress being made in digital transformation, data maturity and AI adoption, with India more advanced than both the US and UK.

Methodology The report is based on a survey of 775 decision makers (senior managers and above) from the US, UK or India. Respondents were all from companies with 100 or more staff. Research was conducted for Peak by Opinium, in partnership with the Center for Economics and Business Research (Cebr). The survey ran from 19-25 April, 2022.

About Peak Jointly founded in Manchester and Jaipur in 2015 by Richard Potter, David Leitch and Atul Sharma, Peak is on a mission to change the way the world works.

A pioneer of the Decision Intelligence category, its platform enables customers to apply AI to the commercial decision making process. Peak features three products Dock, Factory, and Work that can be leveraged by businesses at every stage of their AI journey to build apps that deliver against specific business needs. With features to support both technical and line-of-business users, Peak makes these apps widely accessible to everyone within a business, simplifying and accelerating adoption of AI. It is used by leading brands including Nike, ASOS, PepsiCo, KFC and Sika.

The company has grown significantly over the last three years and, in August 2021, Peak announced a $75m Series C funding round led by SoftBanks Vision Fund II. The same year, it received a Best Companies 3-star accreditation, which recognises extraordinary levels of employee engagement and was ranked by The Sunday Times as one of the Best 100 Companies to Work For in 2020 and 2021.

1 Statista, 2022. Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025 (in zettabytes). Statista.

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Mica Hahnpeak@praytellagency.com510.206.3932

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Commercial AI Adoption is Picking Up Pace But Only A Third of Businesses Recognise that Data Strategy Facilitates AI - GlobeNewswire

On May 12, the Equal Employment Opportunity Commission and the U.S. Department of Justice issued guidance to caution employers about using artificial intelligence (AI) and software tools to make employment decisions. The guidance, titled The Americans with Disabilities Act and the Use of Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees, warns that using these tools without safeguards could result in an Americans with Disabilities Act violation.

The ADA requires that people with disabilities have full access to public and private services and facilities. It also bans employers from discriminating on the basis of disabilities, and requires that they provide reasonable accommodations to employees with disabilities to enable them to do a job.

Companies increasingly rely on software and AI to monitor employees locations, productivity, and performance, such as surveillance monitoring that tracks employees time on task. Employers may then use this information to make pay, disciplinary, and termination decisions. The guidance warns employers that doing so may result in discrimination against employees with disabilities.

The EEOC advises that employers should not be using algorithms, software or AI to make employment decisions without giving employees a chance to request reasonable accommodations. Heres why: Software and AI are designed to provide information based on preset specifications of the average or ideal worker. However, employees with disabilities may not work under typical conditions if they are utilizing an accommodation.

To use these tools without running afoul of the law, employers should train their HR personnel, managers and supervisors to recognize and respond to requests for accommodations from employees, even when the request is informal. This could include an employees requests to take a test in an alternative format, to be assessed in another way, or to be allowed to spend extra time off task due to a medical condition, for example. Employers should also ensure they use software that has been tested by users with disabilities. Other tips for staying in compliance include providing clear instructions to employees for requesting accommodations and avoiding pre-employment screening for traits that may reveal disabilities.

Employers would do well to remember that employees are humans, and sometimes decisions about humans need to be made by humans, not by computers. Metrics tracked by computers should be just one piece of the puzzle when evaluating employees performance.

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Algorithms, AI, and the ADA | McAfee & Taft - JDSupra - JD Supra

As the demand for AI-powered apps grows, startups developing dedicated chips to accelerate AI workloads on-premises are reaping the benefits. A recent ZDNet piece reaffirms that the AI edge chip market is booming, fueled by staggering venture capital financing in the hundreds of millions of dollars. EdgeQ, Kneron and Hailo are among the dozens of upstarts vying for customers, the last of which nabbed $136 million in October as it doubles down on new opportunities.

Another company competing in the increasingly saturated segment is SiMa.ai, which is developing a system-on-chip platform for AI applications particularly computer vision applications. After emerging from stealth in 2019, SiMa.ai began demoing an accelerator chipset that combines traditional compute IP from Arm with a custom machine learning accelerator and dedicated vision accelerator, linked via a proprietary interconnect,

To lay the groundwork for future growth, SiMa.ai today closed a $30 million additional investment from Fidelity Management & Research Company, with participation from Lip-Bu Tan (whos joining the board) and previous investors, concluding the startups Series B. It brings SiMa.ais total capital raised to $150 million.

The funding will be used to accelerate scaling of the engineering and business teams globally, and to continue investing in both hardware and software innovation, founder and CEO Krishna Rangasayee told TechCrunch in an email interview. The appointment of Lip-Bu Tan as the newest member of SiMa.ais board of directors is a strategic milestone for the company. He has a deep history of investing in deep tech startups that have gone on to disrupt industries across AI, data, semiconductors, among others.

Rangasayee spent most of his career in the semiconductor industry at Xilinx, where he was GM of the companys overall business. An engineer by trade Rangasayee was the COO at Groq and once headed product planning at Altera, which Intel acquired in 2015 he says that he was motivated to start SiMa.ai by the gap he saw in the machine learning market for edge devices.

I founded SiMa.ai with two questions: What are the biggest challenges in scaling machine learning to the embedded edge? and How can we help?, Rangasaye said. By listening to dozens of industry-leading customers in the machine learning trenches, SiMa.ai developed a deep understanding of their problems and needs like getting the benefits of machine learning without a steep learning curve, preserving legacy applications along with future proof ML implementations, and working with a high-performance, low-power solution in a user-friendly environment.

SiMa.ai aims to work with companies developing driverless cars, robots, medical devices, drones and more. The company claims to have completed several customer engagements and last year announced the opening of a design center in Bengaluru, India, as well as a collaboration with the University of Tbingen to identify AI hardware and software solutions for ultra-low energy consumption.

As over-100-employee SiMa.ai works to productize its first-generation chip, work is underway on the second-generation architecture, Rangasayee said.

SiMa.ais software and hardware platform can be used to enable scaling machine learning to [a range of] embedded edge applications. Even though these applications will use many diverse computer vision pipelines with a variety of machine learning models, SiMa.ais software and hardware platform has the flexibility to be used to address these, Rangasayee added. SiMa.ais platform addresses any computer vision application using any model, any framework, any sensor, any resolution [We as a company have] seized the opportunity to disrupt the burgeoning edge computing space in pursuit of displacing decades-old technology and legacy incumbents.

SiMa.ais challenges remain mass manufacturing its chips affordably and beating back the many rivals in the edge AI computing space. (The companys says that it plans to ship mass-produced production volumes of its first chip sometime this year.) But the startup stands to profit handsomely if it can capture even a sliver of the sector. Edge computing is forecast to be a $6.72 billion market by 2022, accordingto Markets and Markets. Its growth will coincide with that of the deep learning chipset market, whichsome analysts predict will reach $66.3 billion by 2025.

Machine learning has had a profound impact on the cloud and mobile markets over the past decade and the next battleground is the multi-trillion-dollar embedded edge market, Tan said in a statement. SiMa.ai has created a software-centric, purpose-built platform that exclusively targets this large market opportunity. SiMa.ais unique architecture, market understanding and world-class team has put them in a leadership position.

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AI chip startup SiMa.ai bags another $30M ahead of growth - TechCrunch

A century ago, English mathematician Lewis Fry Richardson proposed a startling idea for that time: constructing a systematic process based on math for predicting the weather. In his 1922 book, Weather Prediction By Numerical Process, Richardson tried to write an equation that he could use to solve the dynamics of the atmosphere based on hand calculations.

It didnt work because not enough was known about the science of the atmosphere at that time. Perhaps some day in the dim future it will be possible to advance the computations faster than the weather advances and at a cost less than the saving to mankind due to the information gained. But that is a dream, Richardson concluded.

A century later, modern weather forecasts are based on the kind of complex computations that Richardson imagined and theyve become more accurate than anything he envisioned. Especially in recent decades, steady progress in research, data and computing has enabled a quiet revolution of numerical weather prediction.

For example, a forecast of heavy rainfall two days in advance is now as good as a same-day forecast was in the mid-1990s. Errors in the predicted tracks of hurricanes have been cut in half in the last 30 years.

There still are major challenges. Thunderstorms that produce tornadoes, large hail or heavy rain remain difficult to predict. And then theres chaos, often described as the butterfly effect the fact that small changes in complex processes make weather less predictable. Chaos limits our ability to make precise forecasts beyond about 10 days.

As in many other scientific fields, the proliferation of tools like artificial intelligence and machine learning holds great promise for weather prediction. We have seen some of whats possible in our research on applying machine learning to forecasts of high-impact weather. But we also believe that while these tools open up new possibilities for better forecasts, many parts of the job are handled more skillfully by experienced people.

Today, weather forecasters primary tools are numerical weather prediction models. These models use observations of the current state of the atmosphere from sources such as weather stations, weather balloons and satellites, and solve equations that govern the motion of air.

These models are outstanding at predicting most weather systems, but the smaller a weather event is, the more difficult it is to predict. As an example, think of a thunderstorm that dumps heavy rain on one side of town and nothing on the other side. Furthermore, experienced forecasters are remarkably good at synthesizing the huge amounts of weather information they have to consider each day, but their memories and bandwidth are not infinite.

Artificial intelligence and machine learning can help with some of these challenges. Forecasters are using these tools in several ways now, including making predictions of high-impact weather that the models cant provide.

In a project that started in 2017 and was reported in a 2021 paper, we focused on heavy rainfall. Of course, part of the problem is defining heavy: Two inches of rain in New Orleans may mean something very different than in Phoenix. We accounted for this by using observations of unusually large rain accumulations for each location across the country, along with a history of forecasts from a numerical weather prediction model.

We plugged that information into a machine learning method known as random forests, which uses many decision trees to split a mass of data and predict the likelihood of different outcomes. The result is a tool that forecasts the probability that rains heavy enough to generate flash flooding will occur.

We have since applied similar methods to forecasting of tornadoes, large hail and severe thunderstorm winds. Other research groups are developing similar tools. National Weather Service forecasters are using some of these tools to better assess the likelihood of hazardous weather on a given day.

Researchers also are embedding machine learning within numerical weather prediction models to speed up tasks that can be intensive to compute, such as predicting how water vapor gets converted to rain, snow or hail.

Its possible that machine learning models could eventually replace traditional numerical weather prediction models altogether. Instead of solving a set of complex physical equations as the models do, these systems instead would process thousands of past weather maps to learn how weather systems tend to behave. Then, using current weather data, they would make weather predictions based on what theyve learned from the past.

Some studies have shown that machine learning-based forecast systems can predict general weather patterns as well as numerical weather prediction models while using only a fraction of the computing power the models require. These new tools dont yet forecast the details of local weather that people care about, but with many researchers carefully testing them and inventing new methods, there is promise for the future.

There are also reasons for caution. Unlike numerical weather prediction models, forecast systems that use machine learning are not constrained by the physical laws that govern the atmosphere. So its possible that they could produce unrealistic results for example, forecasting temperature extremes beyond the bounds of nature. And it is unclear how they will perform during highly unusual or unprecedented weather phenomena.

And relying on AI tools can raise ethical concerns. For instance, locations with relatively few weather observations with which to train a machine learning system may not benefit from forecast improvements that are seen in other areas.

Another central question is how best to incorporate these new advances into forecasting. Finding the right balance between automated tools and the knowledge of expert human forecasters has long been a challenge in meteorology. Rapid technological advances will only make it more complicated.

Ideally, AI and machine learning will allow human forecasters to do their jobs more efficiently, spending less time on generating routine forecasts and more on communicating forecasts implications and impacts to the public or, for private forecasters, to their clients. We believe that careful collaboration between scientists, forecasters and forecast users is the best way to achieve these goals and build trust in machine-generated weather forecasts.

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AI and machine learning are improving weather forecasts, but they won't replace human experts - The Conversation Indonesia

World Weather Attribution

It sounds like a contradiction in terms, but disaster and disruption management is a thing. Disaster and disruption are precisely what ensues when catastrophic natural events occur, and unfortunately, the trajectory the world is on seems to be exacerbating the issue. In 2021 alone, the US experienced15+ weather/climate disaster eventswith damages exceeding $1 billion.

Previously, we have explored various aspects of the ways data science and machine learning intertwine with natural events -- fromweather predictiontothe impact of climate change on extreme phenomenaandmeasuring the impact of disaster relief. AiDash, however, is aiming at something different: helping utility and energy companies, as well as governments and cities, manage the impact of natural disasters, including storms and wildfires.

We connected with AiDash co-founder and CEO Abhishek Singh to learn more about its mission and approach, as well its newly releasedDisaster and Disruption Management System (DDMS).

Singh describes himself as a serial entrepreneur with multiple successful exits. Hailing from India, Singh founded one of the world's first mobile app development companies in 2005 and then an education tech company in 2011.

Following the merger of Singh's mobile tech company with a system integrator, the company was publicly listed, and Singh moved to the US. Eventually, he realized that power outages are a problem in the US, with the wildfires of 2017 were a turning point for him.

That, and the fact that satellite technology has been maturing -- with Singh marking 2018 as an inflection point for the technology -- led to founding AiDash in 2020.

AiDash notes that satellite technology has reached maturity as a viable tool. Over 1,000 satellites are launched every year, employing various electromagnetic bands, including multispectral bands and synthetic aperture radar (SAR) bands.

The company uses satellite data, combined with a multitude of other data, and builds products around predictive AI models to allow preparation and resource placement, evaluate damages to understand what restoration is needed and which sites are accessible and help plan the restoration itself.

AiDash uses a variety of data sources. Weather data, to be able to predict the course storms take and their intensity. Third-party or enterprise data, to know what assets need to be protected and what their locations are.

Also:The EU AI Act could help get to Trustworthy AI, according to the Mozilla Foundation

The company's primary client thus far has been utility companies. For them, a common scenario involves damages caused by falling trees or floods. Vegetation, in general, is a key factor in AiDash AI models but not the only one.

As Singh noted, AiDash has developed various AI models for specific use cases. Some of them include an encroachment model, an asset health model, a tree health model and an outage prediction model.

Those models have taken considerable expertise to develop. As Singh noted, in order to do that, AiDash is employing people such as agronomists and pipeline integrity experts.

"This is what differentiates a product from a technology solution. AI is good but not good enough if it's not domain-specific, so the domain becomes very important. We have this team in-house, and their knowledge has been used in building these products and, more importantly, identifying what variables are more important than others", said Singh.

To exemplify the application of domain knowledge, Singh referred to trees. As he explained, more than 50% of outages that happen during a storm are because of falling trees. Poles don't normally fall on their own -- generally, it's trees that fall on wires and snap them or cause poles to fall. Therefore, he added that understanding trees is more important than understanding the weather in this context.

"There are many weather companies. In fact, we partner with them -- we don't compete with them. We take their weather data, and we believe that the weather prediction model, which is also a complicated model, works. But then we supplement that with tree knowledge", said Singh.

In addition, AiDash uses data and models about the assets utilities manage. Things such as what parts may break when lightning strikes, or when devices were last serviced. This localized, domain-specific information is what makes predictions granular. How granular?

Also:Averting the food crisis and restoring environmental balance with data-driven regenerative agriculture

Supplementing data and AI models with domain-specific knowledge, in this case knowledge about trees, is what makes the difference for AiDash

"We know each and every tree in the network. We know each and every asset in the network. We know their maintenance history. We know the health of the tree. Now, we can make predictions when we supplement that with weather information and the storm's path in real-time. We don't make a prediction that Texas will see this much damage. We make a prediction that this street in this city will see this much damage," Singh said.

In addition to utilizing domain knowledge and a wide array of data, Singh also identified something else as key to AiDash's success: serving the right amount of information to the right people the right way. All the data live and feed the elaborate models under the hood and are only exposed when needed -- for example if required by regulation.

For the most part, what AiDash serves is solutions, not insights, as Singh put it. Users access DDMS via a mobile application and a web application. Mobile applications are meant to be used by people in the field, and they also serve to provide validation for the system's predictions. For the people doing the planning, a web dashboard is provided, which they can use to see the status in real-time.

Also:H2O.ai brings AI grandmaster-powered NLP to the enterprise

DDMS is the latest addition toAiDash's product suite, including the Intelligent Vegetation Management System, the Intelligent Sustainability Management System, the Asset Cockpit and Remote Monitoring & Inspection. DDMS is currently focused on storms and wildfires, with the goal being to extend it to other natural calamities like earthquakes and floods, Singh said.

The company's plans also include extending its customer base to public authorities. As Singh said, when data for a certain region are available, they can be used to deliver solutions to different entities. Some of these could also be given free of charge to government entities, especially in a disaster scenario, as AiDash does not incur an incremental cost.

AiDash is headquartered in California, with its 215 employees spread in offices in San Jose and Austin in Texas, Washington DC, London and India. The company also has clients worldwide and has been seeing significant growth. As Singh shared, the goal is to go public around 2025.

Link:

Managing disaster and disruption with AI, one tree at a time - ZDNet

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'Muhammad Ali The Next Legends' is part of ASM's new long-term partnership with Authentic Brands Group (ABG), which owns Muhammad Ali Enterprises in partnership with Lonnie Ali, who is a trustee of the Muhammad Ali Family Trust. The game will be driven by ASM's artificial intelligence protocol, while web3 creative factory Non-Fungible Labs leads the visual game and character design. This partnership is the first of many for ABG's brands within the ASM ecosystem.

When 'Muhammad Ali The Next Legends' launches, players will be able to collect unique NFT boxers powered by ASM's artificial intelligence, train them and compete for the first time in a fully AI, web3 combat experience. Players will eventually need to unite two NFTs to play the game an ASM Brain, which is also an NFT, with their boxer. Utilizing "Artificial Intelligence Gyms," a signature component of the ASM Protocol, owners will train their brain on the skills needed to win a match (i.e., agility, endurance, stance, sparring, jabs, hooks and uppercuts). Following in the footsteps of the 3-time world champion, the goal of the game will be to train and develop your boxer and win matches against other boxers to eventually become "The Next Legend." Each 3D boxer is completely unique in both the physical character model and its mental ability. Minting dates for boxers and additional game details about the game will be revealed in the coming months.

"We are incredibly excited and honored to welcome world-renowned icon Muhammad Ali into web3," says David McDonald, CEO of ASM. "ABG's portfolio of world-leading brands make them the ideal partner to introduce NFT-curious consumers to the metaverse. Our Non-Fungible Intelligence powers and revolutionizes web3 gaming by allowing NFT owners to train their characters, producing limitless possibilities and interoperable use cases in the metaverse."

"We are thrilled for fans to immerse themselves in the world of 'Muhammad Ali The Next Legends'," said Marc Rosen, President, Entertainment at ABG. "ABG is delighted to kick off this unique partnership with ASM by using the strength of our brands to positively contribute to storytelling behind ASM's innovative technology."

"Our team is so proud to be on the cutting edge creating an entire digital world inspired by Muhammad Ali," says Alex Smeele, CEO at Non-Fungible Labs. "The Next Legends project is a huge endeavor as we are working to ensure every single aspect and character is unique and one-of-a-kind. These boxers have distinct personalities and characteristics and it's been an adventure for our team to build."

About Altered State MachineAltered State Machine (ASM) are leaders in the democratization of Artificial Intelligence, creating a world where the value of A.I. flows directly back to the community. ASM's revolutionary protocol allows everyone to own and utilize A.I. via NFT Brains, also known as Non-Fungible IntelligenceTM. Non-Fungible IntelligenceTM is interoperable across avatars, games, worlds and other Web3 applications, and is trained and owned by each individual user. Join the movement for decentralized Artificial Intelligence with Altered State Machine. Follow ASM on Twitter, Instagramand Discord.

About Non-Fungible LabsNon-Fungible Labs is a web3 dream factory based in Auckland, New Zealand. With an existing ecosystem of successful projects, including FLUF World, NF Labs are working closely with a range of partners to co-create fun and immersive decentralized content on their mission to empower everyday creatives in the development of an open metaverse.

Follow NF Labs on Twitter, Instagramand Facebook.

About Muhammad AliMuhammad Ali is one of the most influential athletes and humanitarians of the 20th century and has created some of the most legendary moments in sports and civil rights history. More than 50 years after he emerged as a Gold Medalist in Boxing at the 1960 Rome Olympics, Ali's legacy extends beyond the ring and he continues to be widely recognized as one of the most celebrated and beloved icons of all time. His incomparable work ethic, signature boxing techniques, and fearlessness towards standing up for his beliefs, all contribute to the legend that is Muhammad Ali. Among his countless awards and accolades, he was named Sports Illustrated's "Sportsman of the Century," GQ's "Athlete of the Century," a United Nations Messenger of Peace, and has received the Presidential Medal of Freedom and the Amnesty International Lifetime Achievement Award. Muhammad Ali's legacy is celebrated across cultures and continues to inspire today's most influential athletes, artists, musicians and humanitarians around the world. Follow Muhammad Ali on Instagram, Facebook andTwitter.

About Authentic Brands GroupAuthentic Brands Group (ABG) is a brand development, marketing and entertainment company, which owns a portfolio of global media, entertainment and lifestyle brands. Headquartered in New York City, with offices around the world, ABG elevates and builds the long-term value of more than 50 consumer brands and properties by partnering with best-in-class manufacturers, wholesalers and retailers. Its brands have a global retail footprint across the luxury, specialty, department store, mid-tier, mass and e-commerce channels and in more than 6,100 freestanding stores and shop-in-shops around the world.

ABG is committed to transforming brands by delivering compelling product, content, business and immersive experiences. It creates and activates original marketing strategies to drive the success of its brands across all consumer touchpoints, platforms and emerging media.

ABG's portfolio of iconic and world-renowned brands generates more than $21.3 billion in global annual retail sales, and includes Marilyn Monroe, Elvis Presley, Muhammad Ali, Shaquille O'Neal, David Beckham, Dr. J, Greg Norman, Neil Lane, Thalia, Sports Illustrated, Reebok, Eddie Bauer, Spyder, Volcom, Airwalk, Nautica, Izod, Forever 21, Aropostale, Juicy Couture, Vince Camuto, Lucky Brand, Nine West, Jones New York, Frederick's of Hollywood, Adrienne Vittadini, Van Heusen, Arrow, Tretorn, Tapout, Prince, Vision Street Wear, Brooks Brothers, Barneys New York, Judith Leiber, Herve Leger, Frye, Hickey Freeman, Hart Schaffner Marx, Thomasville, Drexel and Henredon.

For more information, visit authenticbrands.com.Follow ABG on Twitter, LinkedIn and Instagram.

Authentic Brands/Muhammad Ali Contact:Michelle Ciciyasvili[emailprotected]

Altered State Machine Contact:Chelsey Northern[emailprotected]

SOURCE Authentic Brands Group

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ALTERED STATE MACHINE ANNOUNCES THE FIRST-EVER METAVERSE AI BOXING GAME "MUHAMMAD ALI -- "THE NEXT LEGENDS" - PR Newswire