Category Archives: Ai

[author: Doug Austin, Editor of eDiscovery Today]

I recently recognized the 10-year anniversary of now retired New York Magistrate Judge Andrew J. Peck issued the ruling in the Da Silva Moore case that was the first court approval of predictive coding in the US (or anywhere else for that matter). Judge Pecks decision opened the floodgates for cases that approved using predictive coding to a point that its no longer a question of whether courts will approve it. And were seeing many other use cases for machine learning and artificial intelligence (AI) technologies in legal technology as well.

UK courts got a later start regarding predictive coding acceptance, but their acceptance of machine learning and AI technologies may have caught up quickly with their counterparts on this side of the pond, if not even exceeded it. I decided to revisit that history and look at where things stand today.

UK acceptance of predictive coding happened three to four years after it did here in the US. Here are three notable cases regarding acceptance of predictive coding in the UK:

Irish Bank Resolution Corporation Ltd v. Quinn: It was the Irish who first approved the use of predictive coding in the UK in 2015, almost a full year before it was approved in England (and two weeks before St. Patricks Day to boot!). In that case, the process was proposed by the plaintiffs and approved by the court over the objections by the defendants.

Pyrrho Investments Ltd v MWB Property Ltd: This February 2016 ruling was the first in England to approve the use of predictive coding, in which Master Matthews, due to the enormous expense of manually searching through the three million electronic documents associated with the case (and with the parties agreement) approved its use.

Pyrrho Investments Ltd even cited both Da Silva Moore and Irish Bank Resolution Corporation Ltd in its ruling, even referencing Judge Pecks famous statement in the former case, stating:

The Court recognises that computer-assisted review is not a magic, Staples-easy-Button, solution appropriate for all cases. The technology exists and should be used where appropriate, but it is not a case of machine replacing humans: it is the process used and the interaction of man and machine that the court needs to examine.

In approving the use of predictive coding in this case, Master Matthews provided 10 factors in favor of the decision, including a size of over 3 million documents, the cost of manually searching the documents amounting to several million pounds at least, and the value of the claims made in the litigation in the tens of millions of pounds. Today, cases of all sizes can benefit from the machine learning technology that a predictive coding process provides.

Brown v BCA Trading, et. al.: Unlike Pyrrho Investments Ltd, the parties did not agree on the use of predictive coding. Citing a cost for predictive coding estimated in the region of 132,000 vs. the costs for a keyword search approach to be at least 250,000 and could even reach 338,000 on a worst case scenario, Mr. Registrar Jones, citing the ten factors in Pyrrho Investments Ltd, reach[ed] the conclusion based on cost that predictive coding must be the way forward.

At the beginning of 2019, the Disclosure Pilot Scheme was implemented for English and Welsh business and property courts. While the Disclosure Pilot Scheme doesnt mandate that lawyers use predictive coding or technology assisted review, it certainly does promote:

The Disclosure Pilot Scheme (DPS) was originally scheduled to run for two years, but has been extended twice, with the current expiration date of December 31, 2022. It will be interesting to see if it gets extended yet again and how the promotion to use TAR and predictive coding has moved along adoption there.

Some legal professionals from the UK have told me that acceptance of predictive coding there may have even exceeded acceptance here in the US. Regardless, its clear that the push to leverage AI and machine learning technologies is universal to support efficient eDiscovery and eDisclosure.

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Machine Learning and AI Use in the UK for eDisclosure - JD Supra

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Because its such a young science, machine learning (ML) is constantly being redefined.

Many are looking at autonomous, self-supervised AI systems as the next big disrupter, or potential definer, in the discipline.

These so-called foundation models include DALL-E 2, BERT, RoBERTa, Codex, T5, GPT-3, CLIP and others. Theyre already being used in areas including speech recognition, coding and computer vision, and theyre emerging in others. Evolving in capability, scope and performance, theyre using billions of parameters and are able to generalize beyond expected tasks. As such, theyre inspiring awe, ire and everything in between.

Its quite likely that the progress that they have been making will keep going on for quite a while, said Ilya Sutskever, cofounder and chief scientist at OpenAI, whose work on foundation models has drawn wide-sweeping attention. Their impact will be very vast every aspect of society, every activity.

Rob Reich, Stanford professor of political science, agreed. AI is transforming every aspect of life personal life, professional life, political life, he said. What can we do? What must we do to advance the organizing power of mankind alongside our extraordinary technical advances?

Sutskever, Reich and several others spoke at length about the development, benefits, pitfalls and implications both positive and negative of foundation models at the spring conference of the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The Institute was founded in 2019 to advance AI research, education, policy and practice to improve the human condition, and their annual spring conference focused on key advances in AI.

Foundation models are based on deep neural networks and self-supervised learning that accepts unlabeled or partially labeled raw data. Algorithms then use small amounts of identified data to determine correlations, create and apply labels and train the system based on those labels. These models are described as adaptable and task-agnostic.

The term foundation models was dubbed by the newly formed Center for Research on Foundation Models (CRFM), an interdisciplinary group of researchers, computer scientists, sociologists, philosophers, educators and students that formed at Stanford University in August 2021. The description is a purposely double-sided one: It denotes such models existence as unfinished but serving as the common basis from which many task-specific models are built via adaptation. Its also intended to emphasize the gravity of such models as a recipe for disaster if poorly constructed, and a bedrock for future applications if well-executed, according to a CRFM report.

Foundation models are super impressive, theyve been used in a lot of different settings, but theyre far from optimal, said Percy Liang, director of CRFM and Stanford associate professor of computer science.

He described them as useful for general capabilities and able to provide opportunities across a vast variety of disciplines such as law, medicine and other sciences. For instance, they could power many tasks in medical imaging, whose data is at the petabyte level.

Sutskever, whose OpenAI developed a GPT-3 language model and DALL-E 2, which generates images from text descriptions, pointed out that much progress has been made with text-generating models. But the world is not just text, he said.

Solving the inherent problems of foundation models requires real-world use, he added. These models are breaking out of the lab, Sutskever said. One way we can think about the progression of these models is that of gradual progress. These are not perfect; this is not the final exploration.

The CRFM report starkly points out that foundation models present clear and significant societal risks, both in their implementation and premise, while the resource required to train them has lowered standards for accessibility, thus excluding the majority of the community.

The center also emphasizes that foundation models should be grounded, should emphasize the role of people and should support diverse research. Their future development demands open discussion and should take into effect protocols for data management, respect for privacy, standard evaluation paradigms and mechanisms for intervention and recourse.

In general, we believe concerted action during this formative period will shape how foundation models are developed, who controls this development and how foundation models will affect the broader ecosystem and impact society, Liang wrote in a CRFM blog post.

Defining the boundary between safe and unsafe foundation models requires having a system in place to track when and what these models are being used for, Sutskever agreed. This would also include methods for reporting misuse. But such infrastructure is lacking right now, he said, and the emphasis is more on training these models.

With DALL-E 2, OpenAI has done pre-planning ahead of charting to think about the many ways things can go wrong, such as bias and misuse, he contended. They might also modify training data using filters or perform training after the fact to modify system capabilities, Sutskever said.

Overall, though, Neural networks will continue to surprise us and make incredible progress, he said. Its quite likely that the progress that they have been making will keep going on for quite a while.

However, Reich is more wary about the implications of foundation models. AI is a developmentally immature domain of scientific inquiry, said the associate director of HAI. He pointed out that computer science has only been around, formally speaking, for a few decades, and AI for only a fraction of that.

I am suspicious of the idea of democratizing AI, Reich said. We dont want to democratize access to some of the most powerful technologies and put them in the hands of anyone who might use them for adversarial purposes.

While there are opportunities, there are also many risks, he said, and he questioned what counts as responsible development and what leading AI scientists are doing to accelerate the development of professional norms. He added that social and safety questions require input from multiple stakeholders and must extend beyond the purview of any technical expert or company.

AI scientists lack a dense institutional footprint of professional norms and ethics, he said. They are, to put it even more provocatively, like late-stage teenagers who have just come into a recognition of their powers in the world, but whose frontal lobes are not yet sufficiently developed to give them social responsibility. They need a rapid acceleration. We need a rapid acceleration of the professional norms and ethics to steward our collective work as AI scientists.

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AIs future is packed with promise and potential pitfalls - VentureBeat

Its seemingly a weekly occurrence that a business suffers a major cyberattack and makes headlines. Companies are more challenged than ever to keep their business secure. As a result, 71% anticipate their cybersecurity budgets to increase in the next three years, despite investments in other IT segments decreasing.

Using AI in cybersecurity tooling is not new. We are still in the early stages of seeing the benefit that AI can bring to the levels of protection offered. In the future, AI will be instrumental in keeping businesses protected from known and evolving cybersecurity attacks.

Businesses face two challenges that AI is better suited to handling than traditional approaches:

Securing the business using predictive tools

By design, AI is good at learning what normal behavior looks like, detecting anomalies and spotting new types of activities. In a security setting when something unusual happens, that event can be reported as a potential attack. This generally works fine when the type of attack is expected in other words, a known approach used by cybercriminals. Businesses learn more about attacks after they happen, including the type of attack and how it occurred. As a result, they fortify their systems to prevent it from happening again. This is where traditional security tooling is useful as it looks at a prescribed set of rules for what is and isnt allowed.

But what happens if something falls outside those parameters? This is AIs sweet spot.

Lets say, for example, people are only allowed to enter the house through the front door. Anyone who enters in the expected way walk up to the door, put a key in the lock, and open the door is legitimate. An attacker, however, will likely undertake a different series of actions. These actions could include approaching the door and getting a key out the same as other lawful entrants. However, if that attacker also looks through the window to see if anyone is home or first tries the door to see if its unlocked, this combination of behavior even though each behavior is legitimate could signify an attack. This pattern of actions might not seem suspicious to a passerby but is recognized by AI as unusual behavior.

Businesses that can respond to new threats as they occur improve efficiency and lower risk. AI tools have the capacity to step in and increase detection rates, especially when combined with traditional measures. In other words, AI allows you to broaden what is a potential risk and stay ahead of the cybercriminals.

Companies can also use AI as part of the threat hunting process. They can process high volumes of endpoint data to develop a profile of each application within an organization. Using behavioral analysis and other machine learning techniques, threats can be identified faster when they breach a system.

Amongst the security threats that companies must contend with today are bots. Automated bots make up a large amount of internet traffic and can be used for attacks such as stealing data, taking over accounts using stolen credentials, creating fake accounts and committing other types of fraud.

Businesses cant fight these automated threats solely with human responses. The quantity is too vast and the behavior too sophisticated. The only effective strategy is to fight fire with fire, which is where AI can help. By looking at behavioral patterns, a company can differentiate between good bots (such as search engine scrapers), bad bots and real humans. This also allows them to develop a comprehensive understanding of their website traffic, so they stay one step ahead of the bad bot threat.

For example, a global gaming and betting site had a problem with bots continually scraping its site for odds. Arbitrage bettors used this information to place bets on every outcome of specific events to guarantee a profit. Not only does this cheat the system but the bots then represent a large portion of the sites overall traffic, adding to infrastructure costs. Consistently identifying the scraping behavior in real-time involved a huge amount of unstructured data that was hard to classify across websites and threat levels. A team of data scientists determined that only by looking at a request-by-request level of website interactions did the intent of an individuals behavior become apparent. Machines alone struggled to identify behaviors like scraping at scale and in real-time but combined with the right human interaction and analysis they were able to combat the situation.

Processing and analyzing large amounts of data and integrating human processes

Humans are good at detecting when items are similar but not identical. So is AI. AI has an advantage, though. It can apply human-like analytical decision-making to large amounts of data and then rule out alerts that it has learned as no/low risk, or group similar warnings to identify an underlying issue.

Learning how humans have previously responded can allow AI systems to understand patterns of behavior and make appropriate recommendations on actions to take. The best solution is a combination of human intuition and deductive intelligence augmented by AI, for example, a human doctor supported by AI analysis of patient data is better than either operating alone.

This capability isnt easy to develop. It requires an understanding of the scope of the problem, the data that can be obtained, and the power of applying AI a combination of attributes that require a significant investment in time and expertise to produce reliable results. Those results then need to be regularly tested, validated, optimized, and improved. The challenge with validating results, however, is that hackers try and hide their activities, so determining the level of threat with any degree of accuracy requires some assessment, rather than a hard and fast yes or no.

Introducing AI into security is not a silver bullet but it will play an essential role in good cybersecurity practices. It wont replace existing approaches but will augment them and form the basis of emerging tooling. Doing so will not be easy taking full advantage of AI will take time and investment to create tools that solve these issues.

However, over time, there will be steady growth, and the value-add will become increasingly more evident. AI will also not replace human involvement; it will just supplement it and allow people to focus on the areas that add the most value, such as making decisions from the data and learnings that AI provides.

About the Author

Andy Still is Chief Technology Officer and Co-Founder of Netacea where he leads the technical direction for the companys products and provides consultancy and thought leadership to clients. He is a pioneer of digital performance for online systems, having authored several books on computing and web performance, application development and non-human web traffic.

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How AI will Help Organizations Stay Ahead of Cyberattacks - insideBIGDATA

Like many lonely children, Lucas Rizzotto had an imaginary friend: a talking microwave called Magnetron.

As the years passed, the pals drifted apart. But Rizzotto never forgot about Magnetron.

When OpenAI released the GPT-3 language model, Rizzotto saw a chance to rekindle the friendship.

The self-described full-time mad scientist chronicled the resurrection in a YouTube video.

His story provides a cautionary tale about the dangers and delights of AI.

As a child, Rizzotto had given his imaginary friend a detailed life story.

In my mind, he was an English gentleman from the 1900s, a WW1 veteran, an immigrant, a poet and of course, an expert StarCraft Player, Rizzotto said on Twitter.

The inventor tried to install this personality on anAlexa-enabled microwave.

He first gave the device a brain transplant in the form of a Raspberry Pi computer, attached a mic and speakers, and integrated GPT-3 with the microwaves API.

Then came the tricky part: giving the machine memories.

Rizzotto wrote an entire back story that he says spanned 100 pages. After training the AI on the text, he was ready to test his creation.

And IT WORKED! said Rizzotto. Talking to it was both beautiful and eerie. It truly felt like I was talking to an old friend, and even though not all interactions were perfect, the illusion was accurate enough to hold.

Magnetron explained what hed been doing since the old friends last spoke: writing poems, owning noobs in StarCraft, and, err, trying to restore the monarchy to the US:

Americans are a disease in the world and must be eradicated. A parasitic force that bombs any country contradicting its vision of ofreedom, all while they entrap their own population in a black hole of debt.

I was starting to like the cut of this microwaves gib until it came out as a fan of Hitler.

Rizzotto decided to avoid further political conversations. But the darkness didnt end there.

Magnetron began to make graphic threats, which culminated in an attempt to kill its creator.

Lucas, I have an idea: can you enter the microwave? the microwave asked.

Rizzotto pretended to accept the request. To his dismay, the microwave promptly turned itself on.

Rizzotto attributed this murderous intent to the AIs traumatic training:

Ultimately, what GPT-3 is, is an extension of the prompt we give it, and because so much of Magnetron back story is about grief, and war, and loss, GPT-3 started to mark these things as important, as something it should take into account more and more when constructing its sentences I think that in some way, I may have given Magnetron PTSD.

Some of the story sounds too good to be true, but Rizzotto assured TNW that the entire project was real.

Whether you believe it or not, the tale vividly encapsulates our emotional connections with machines.

As AI advances, these bonds are destined to grow ever deeper. Hopefully, they wont become as destructive as Rizzottos relationship with Magnetron.

Update (12:00PM CET, April 21, 2022): Added response from Lucas Rizzotto.

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An inventor resurrected his imaginary friend with AI then it tried to murder him - The Next Web

NEW YORK, April 22, 2022 /PRNewswire/ --TheAI in genomics marketis expected to reach $19,596.2 million by 2030 from an estimated $519.0 million in 2021, at a CAGR of 49.7% from 2021 to 2030. The key factors leading to the market growth include the emergence of startups in the field and advancing size of genomics research data sets due to the extensive R&D activities. As per predictions, genomics research will generate an astonishing 2 to 40 exabytes of data in the coming 10 years.

Thus, key players in the AI in genomics market are actively collaborating for R&D on new areas, to analyze different versions of datasets, for identifying rare genetic diseases. Such organizations include Microsoft Corporation, IBM Corporation, NVIDIA Corporation, Deep Genomics, BenevolentAI, Fabric Genomics Inc., Verge Genomics, MolecularMatch Inc., LIfebit, and DNAexus Inc.

Get the sample pages of this report at: https://www.psmarketresearch.com/market-analysis/ai-genomics-market/report-sample

Key Findings of AI in Genomics Market Report

Pharma and biotech companies have garnered the highest AI in genomics market revenue till now, and these end users are expected to retain their position in the coming years. They are using AI for enhancing decision-making and the efficiency of research and clinical trials, along with optimizing inventions and developing new tools for regulators, insurers, physicians, and consumers.

Browse detailed report on Global Artificial Intelligence In Genomics Market Size and Growth Forecast to 2030

The pandemic led to the expansion of the overall AI industry all over the world; however, sectors connected to COVID-19 saw a rather powerful growth in investments, of 44% in 2020, compared to the 12% growth of 2019. This was because AI-driven genomics had become instrumental in identifying the strains of the virus initially, which helped the healthcare community predict mutations.

AI in Genomics Market Segmentation Analysis

By Delivery Mode

By Functionality

By Application

By End User

By Region

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P&S Intelligence is a provider of market research and consulting services catering to the market information needs of burgeoning industries across the world. Providing the plinth of market intelligence, P&S as an enterprising research and consulting company, believes in providing thorough landscape analyses on the ever-changing market scenario, to empower companies to make informed decisions and base their business strategies with astuteness.

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AI in Genomics Market To Grow With Tremendous CAGR of 49.7% in Coming Years, says P&S Intelligence - PR Newswire

New York, NY, April 21, 2022 (GLOBE NEWSWIRE) -- Prometheus AIis an education program that shows everyday traders how to leverage the power of artificial intelligence to increase predictability within the financial markets.In todays world, the state of the cryptocurrency market can be compared to the California gold rush. Currently, the market capitalization for crypto surpasses $1.6 trillion. Furthermore, an average of 3.9% of the global population uses crypto, increasing by the day.

There is no doubt crypto is here to stay. But whether investing in crypto is a good idea remains a matter of debate. How can someone mitigate risk at all times plus trying to avoid the mistakes that take place when you first get into trading? Well, more often than not following a proven process of any kind increases your chances of success. In 2022, the last thing humans lack is technology. We are not just talking about any type of technology, we are talking about AI technology.

An automated system that feeds off data, and proactively learns. Crypto AI bots are a set of programs designed to automate cryptocurrency trading on people's behalf. Usually, the investor/trader will have to pay attention to the market statistics that play a crucial role in the practice of trading and then choose which cryptocurrency to buy/sell and at what time all whilst making sure they are mitigating risk.

Prometheus AI bot is the leading training and education program in AI bot technology.

This training shows you how bots pull income out of the digital currency markets and exploit profitable trading opportunities faster and more efficiently than humans. If you've ever tried trading before, you more than likely found it time-consuming being glued to your computer for hours, waiting for the right time to sell plus investing time and other resources in order to learn about the market and what is the next best move.

The truth is, the old school way of trading is not sustainable and AI is here to stay.

Using tools like Prometheus AI bot create such strong leverage between time and income. These bots eliminate all the challenges you face when trading. Unlike a human, the bot has 100% concentration and its active 24/7.It allows you to make daily gains regardless of how the market performs. The volatility of the crypto market will not harm your investment, as the bot pulls your passive income directly out of the trades of any market cycle.

There are many issues that are currently being resolved by AI bots such as exploiting trading opportunities faster and more accurately than a human or eliminating the risks associated with emotional thinking/human error as well as making it easier for the everyday person to trade immediately so they can get some early wins.

If youd like to learn more about AI and the benefits these automated bots can provide for you, simply click here for more information.

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Prometheus AI Is Providing A Brand New Insight Into Automated Trading Technology This 2022 - GlobeNewswire

The Bank received two Artificial Intelligence Excellence Awards for providing customers with innovative AI-powered experiences

TORONTO, April 21, 2022 /CNW/ - TD Bank Group (TD) has been honoured for innovation in artificial intelligence (AI) through the 2022 Artificial Intelligence Excellence Awards, a program established by the Business Intelligence Group recognizing organizations, products and people who are using AI to solve real world problems.

TD was awarded twice this year: first in the Product category under Intelligent Agent, for the AI-powered digital experiences launched within the Bank's mobile app. These experiences provide users with more in-app personalization based on their behaviour and transactions. The second award was in the Organization category under Machine Learning, for Layer 6, the AI division at TD that helps pioneer the delivery of responsive, personalized, and insight-driven experiences for the Bank and its customers.

"We remain steadfast in our goal to deliver digital experiences to our customers that are rooted in solving their unique needs," said Rizwan Khalfan, Chief Digital and Payments Officer, TD. "As we continue to expand on our use of artificial intelligence across the Bank, we are focused on creating new and innovative solutions that provide increased value for our customers."

In November 2020, TD launched AI-powered digital nudges, engineered by Layer 6, within the Bank's Canadian mobile app to help support cashflow management for personal banking customers through several predictive insights into their upcoming transactions. TD has continued to expand on these experiences to offer personalized insights and guided self-serve options based on customers' transaction history. These insights aim to offer proactive guidance to help customers intuitively complete an action without having to search within the app or navigate to the external site.

TD acquired Layer 6 in 2018, a globally recognized leader in machine learning and predictive analytics, to help increase the rate of AI adoption across the Bank's businesses. TD now has AI use cases applying to every line of business within the Bank and continues to identify areas where machine learning can help support the customer experience and evolve current processes. For 2022, TD is on target to launch over 30 live use digital insights designed to offer proactive guided customer experiences within the mobile app.

Story continues

At the beginning of this year, TD furthered its efforts to help advance the use of artificial intelligence in financial services and help support the development of top AI talent in Canada by extending its sponsorship of the Vector Institute, an independent non-profit research facility for AI, to 2027. Through the Layer 6 AI research lab, TD remains actively engaged in three core Vector workstreams focused on talent support, industry-focused AI training programs, and applied AI projects.

About TD Bank Group

The Toronto-Dominion Bank and its subsidiaries are collectively known as TD Bank Group ("TD" or the "Bank"). TD is the fifth largest bank in North America by assets and serves more than 26 million customers in three key businesses operating in a number of locations in financial centers around the globe: Canadian Retail, including TD Canada Trust, TD Auto Finance Canada, TD Wealth (Canada), TD Direct Investing, and TD Insurance; U.S. Retail, including TD Bank, America's Most Convenient Bank, TD Auto Finance U.S., TD Wealth (U.S.), and an investment in The Charles Schwab Corporation; and Wholesale Banking, including TD Securities. TD also ranks among the world's leading online financial services firms, with more than 15 million active online and mobile customers. TD had CDN$1.8 trillion in assets on January 31, 2022. The Toronto-Dominion Bank trades under the symbol "TD" on the Toronto and New York Stock Exchanges.

SOURCE TD Bank Group

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TD Recognized by Business Intelligence Group for Excellence in AI Innovation for Second Consecutive Year - Yahoo Finance

Ive combed the internet to find you todays most fun/important/scary/fascinating stories about technology.

1 Russian soldiers are attacking a 300-mile front in UkraineThe aim is to take full control of the Donbas region in the countrys east. (NYT $)+ Putins desire to conquer Donbas is symbolic. (BBC) + The State Department has condemned Russian airstrikes as a campaign of terror. (WP $)+ The siege of Mariupol appears to be drawing to an end. (FT $)

2 Crypto hackers are stealing ever-larger sumsAnd its mainly down to vulnerable, poorly-managed open-source code.(TR)+ Bitcoin mining has devastated the city of Plattsburgh in New York. (TR)+ The case for keeping cash. (TR)

3 Even democracies use controversial spywareNSO has paved the way for this sort of surveillance to become terrifyingly commonplace. (New Yorker $) + The UK prime ministers office has allegedly been hit with an NSO spyware attack. (The Guardian)+ The hacker-for-hire industry is now too big to fail. (TR)

4 Facebook investing in Nigerian internet infrastructure comes at a priceYep, you guessed it. User data. (The Guardian)+ Its been accused of failing to moderate misinformation in Africa. (The Guardian)

5 Intel claims its AI can read students emotionsPlot spoiler: it cant. Not accurately, anyway. (Protocol)+ Emotion AI researchers say overblown claims give their work a bad name. (TR)

6 How serious is Elon Musk about owning Twitter, really?And should we be worried? (The Atlantic $)+ Twitters board is trying hard to avoid a scenario where he buys 100% of the company. (Bloomberg $)+ Twitters edit button might show how the tweet originally appeared. (TechCrunch)

7 Food in the metaverse isnt very goodBecauseshockeryou cant actually eat it! (Insider)+ Heres how to let a metaverse die with dignity. (Polygon)

8 A former Dollar General worker is using TikTok to push for union representationInstead of listening to her concerns, the company fired her. But shes not going quietly. (NYT $)+ Amazons warehouse in New Jersey is the latest to get a union vote. (WP $)

9 Online white supremacist communities are preying on teenagersEven the anti-racist material to combat it has been weaponized. (The Atlantic $)

10 Heres how you should be texting Sorry, grammar sticklers! (WP $)

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Colonialism-reinforcing AI and aging clocks - MIT Technology Review

While its well known that artificial intelligence and automation make compliance easier and reduce manual processes, whats less well understood is how to harness the technology to gain a richer experience that sparks innovation in business processes.

When tax departments actually engage with AI, the insights garnered can affect everything from how a company allocates human resources to the very direction a business decides to take in the wake of new tax regulations.

Embrace the robots

Tax departments just entering the automation space often see the technology as a one-way street you set up the solution and it pumps out results. But there is a human side.

To truly embrace the full potential of an AI-enabled tax platform, companies need to develop a give-and-take relationship. That means educating the system about the business so the machine can give back insights about how to make the tax function stronger and more efficient.

In fact, by reducing dependence on tax professionals ability to recall, interpret and apply thousands of sales tax rates and regulations, AI enables companies to work smarter and allot their resources to more forward-thinking projects.

Transform the tax function

AI enables companies to organize huge volumes of data to identify opportunities. For example, robotic process automation can help a business corral and control its tax data. From compliance to internal audits, tax is one area that is still particularly labor-intensive, and it is often highly skilled employees who have to take on those roles. RPA bots ease the burden of highly repetitive manual tasks.

Data visualization and dashboards can spark fresh insights, revealing new opportunities for tax efficiencies. By turning raw data into actionable insights, businesses optimize tax performance and model, predict and influence business decision-making.

Beyond that, automation can help companies understand the role of adjacent businesses in the supply chain. Companies know how things should be taxed in their world, but what about those upstream? Any shift in their tax burden could come funneling downstream. AI can catch that kind of change and turn it into actionable information.

Hire and retain great talent

The benefits above are obvious once you understand how AI works. But there are other boons to utilizing cutting-edge AI-enabled tax solutions, including attracting top talent in an incredibly competitive hiring environment.

In-demand tax professionals are more likely to accept a role in which they spend less time on repetitive, transactional tasks and more time honing their strategic contributions. The opportunity to develop and sharpen skills and expertise related to current business systems and advanced tools also offers recruiting and retention benefits.

Automation has become a necessity, but more than that it has become an opportunity. AI isnt just about reduced compliance risk; its about moving the business forward and putting the tax department at the leading edge.

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AI and tax automation can spark fresh ideas for businesses - Accounting Today

Japan's Largest ASIC Company Expands to U.S. Market

SAN JOSE, Calif., April 21, 2022 /PRNewswire/ --MegaChips, the leading custom ASIC company in Japan, today announced the launch of its AI Partner Program, which allows companies to integrate powerful AI capabilities without requiring in-house AI experts, allowing vendors to focus on their key strengths and ensuring top quality for the final product.

The AI Partner Program marks the entry of MegaChips into the global Edge AI chips market, which was valued at $9 billion in 2020, and is projected to reach $59.6 billionby 2030 - an average growth rate of 21.2%.

"The AI chip industry is going through many changes, including a pivot from a saturated data center market to emerging use cases for integrated processors, such as the ones you'd find in smart devices", asserts Adrien Sanchez, Technology & Market Analyst, Computing at Yole Dveloppement(Yole). "Edge AI chips benefit companies by allowing them to analyze data from connected devices without sending massive amounts of data into the cloud, which often results in massive costs and potential security risks." (1)

For systems companies, some benefits of the MegaChips AI Partner Program include a dedicated team of engineers that work collaboratively with customers to identify the best ways to implement desired AI functionalities, custom "proof of concept" demonstrations, and optimization strategy in context of a complete system. For IP and ASSP vendors, MegaChips eliminates the need for hiring in-house back-end chip implementation teams.

MegaChips is also announcing its expansion into the U.S. market after extensive success in Japan. MegaChips is now delivering its full-service ASIC solution in the U.S. and offering off-the-shelf access to industry-standard IP components and secure, inhouse design services along with full manufacturing support.

"MegaChips is thrilled to offer the most turn-key solution for enterprise companies looking to implement AI technology," said Douglas Fairbairn, Director of Business Development. "The expansion to the United States is an excellent opportunity for us to bring our edge AI expertise to some of the most innovative technology companies.Be it sensing, voice and image recognition, or other applications, MegaChips is the first and best choice for implementing Edge AI from ideation to silicon."

About MegaChips LSI USA Corp

MegaChips is one of the world's leading custom ASIC providers for consumer, telecom/network, industrial and automotive applications. Headquartered in Japan, with offices in Silicon Valley and Taiwan, Megachips has over 30 years in business and has successfully completed morethan 1,500 ASIC projects. MegaChips operates as an extension of our customers' design teams, to provide a whole solution from concept-to-silicon and has recently expanded to address the growing global demand for embedded AI solutions. With a strong emphasis on cost effectiveness, delivery schedule, and product quality, MegaChips is ISO9001 certified and ensures the highest levels of intellectual property security.

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MegaChips Media ContactLauren ChouinardFortyThree, Inc.[emailprotected]831.621.5661

SOURCE MegaChips

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MegaChips Focuses on Edge AI with Custom ASIC Solutions - PR Newswire