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Capitol Hill Stumps Anthropic on its Google Relationship – The Information

Capitol Hill may not have the best reputation when it comes to being tech-savvy. One may recall Senator Orrin Hatchs infamous question to Meta CEO Mark Zuckerberg about how Facebook stays free (which a smirking Zuckerberg responded to with, Senator, we run ads).

But it was a different story during yesterdays Senate hearing on artificial intelligence regulation, which featured testimonies from Dario Amodei, CEO and cofounder of OpenAI rival Anthropic, Stuart Russell, a University of California, Berkeley computer science professor, and Yoshua Bengio, a Universit de Montral professor. Legislators seemed surprisingly up-to-date.

Connecticut Sen. Richard Blumenthal, for instance, asked Amodei about potentially setting safety breaks on AutoGPT, a viral AI agent that can automatically carry out a multi-step task, such as conducting market research. Missouri Sen. Josh Hawley brought up a recent Wall Street Journal article on the dismal working conditions Kenyan workers faced while helping OpenAI build a safety filter for its models.

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Parkinson’s disease and sleep problems: Finding out if deep brain … – UCHealth Today

Murray Engelkeimer, pictured with his grandson Graham, has been dealing with Parkinsons, which causes a variety of challenging symptoms. Doctors are studying whether deep brain stimulation or DBS can help Parkinsons patients with sleep problems. Photo courtesy of Murray Engelkeimer.

About 20 years ago, Murray Engelkemier made a routine stop to pick up some dry cleaning. The ordinary errand took a turn, however, when he began making out a check for his order.

It was hard to write with my right hand, Engelkemier recalled. He soon discovered that the trembling was the result of Parkinsons disease (PD), which causes degeneration in areas of the brain that control movement. The motor symptoms of PD include the tremor that affected Engelkemier, as well as stiffness, slowed movements, balance problems and more.

Engelkemier, now 65, treated his disease with medications designed to increase the levels of dopamine in his brain. Dopamine is a chemical that plays a key role in nerve signaling that enables movement. His symptoms continued to progress, but the treatments worked well enough that he was able to continue for well over another decade in his role as a project and program manager with U.S. Bank.

I was pretty good until the last three years of my career, Engelkemier said. Over that final period, he said he burned out with monthly work-related travel to Spain, leading him to take disability and retire.

His biggest movement-related problem has been leg stiffness that causes him to shuffle and drag his feet.

My mind is always getting ahead of my body, Engelkemier said. But another problem not related to movement also plagued him: regularly disrupted sleep.

I would wake up at 4 a.m. and could not go back to sleep, Engelkemier said. My mind was racing. The fatigue detracted from my quality of life.

His problem is not unusual, said Dr. Alexander Baumgartner, an assistant professor of Neurology with the University of Colorado School of Medicine who practices with UCHealth.

We think of Parkinsons disease as having a lot of motor symptoms, but there are many other, non-motor symptoms, and sleep is one of the most common, Baumgartner said. He added that people with PD also frequently suffer from mood disorders, cognitive decline and dementia, all of which may be entangled with chronically poor sleep.

Broadly speaking, improving sleep is a way to promote general brain health, and that could decrease the risk of developing dementia in the future, Baumgartner said. However, we dont have great treatments designed for these non-motor symptoms [of PD]. There is an unmet need.

Baumgartner now leads a trial that aims to explore whether deep-brain stimulation (DBS), an established surgical treatment for movement-related problems in people with PD, could also help to improve their sleep and their quality of life.

Baumgartner and his colleagues are recruiting people who are considering DBS, a procedure that involves implanting leads, or electrodes, in the brain and connecting them to another implanted device that generates electrical impulses in particular areas of the brain that control movement. These impulses can help to disrupt and tame the haywire electrical signaling that causes the debilitating movement symptoms of Parkinsons disease.

Baumgartner said there are indications that DBS might also help patients experience more restful nights.

We have seen for many years that people who go through DBS often report that they are sleeping better after the procedure, he said.

The problem is a lack of rigorous research to validate and quantify the sleep-related benefits of DBS, he added. For example, some studies previously conducted after DBS surgery simply asked patients to rate their sleep quality subjectively. A small number of others assigned patients to a one-night stay in a sleep lab, hooked up to a monitor to measure their brain activity. In both cases, researchers were able to glean only limited information.

Baumgartners study, which gathers data from patients before and after their DBS surgery, aims to change that with improved technology. Instead of scheduling time and checking into a sleep lab, recruits complete the trial at home, with a headband device that records their brain activity over a three-night period. The in-home arrangement could improve not only the amount of data collected but also its quality, Baumgartner said.

[Patients] spend the night in their own bed, so they are acting more naturally and following their normal sleep patterns and behaviors, rather than spending the night away from home in an uncomfortable bed in a lab, he said. Were also able to study them over consecutive nights, so were getting more data with people in familiar environments that more closely approximate what their life is truly like.

Recruits also get a Fitbit-like wristband that they wear throughout the day to track their normal activity, another likely indicator of how well or poorly they have slept. More data comes from looking at sleep characteristics when patients are taking their PD medications, and when they are not, Baumgartner added.

The researchers ask the recruits to wear the devices for a series of several nights leading up to their DBS surgery. After the surgery, patients have a period for healing and adjustments of the implanted system to manage their motor symptoms as effectively as possible. After about three months, Baumgartner and his team approach them for their consent to participate in the post-surgical part of the trial. If they agree, they go through the same routine they followed before the DBS procedure.

The headband yields data that help the researchers evaluate what sleep pattern changes, if any, occurred after DBS surgery. The measurements include how much sleep patients got, how long it took them to fall asleep and how often they awoke after falling asleep.

The device also offers clues to the quality of sleep, such as how long patients were in Stage 3, which Baumgartner explained is especially important for restoring and recharging mind and body and maintaining healthy memory function and cognition. Recruits offer their own perspectives through questionnaires that ask about how well they slept, their energy levels throughout the day, their pain levels, how often they had to get up during the night and other ways they evaluate their sleep.

Murray Engelkemier said he was initially resistant to the idea of DBS surgery. I didnt want anybody drilling holes in my head, he said. But as his leg problems, in particular, persisted, his providers at UCHealth encouraged him to speak with other patients who had had the procedure, and he was impressed by the positive results they reported.

He decided to seek an evaluation in the spring of 2022, and neurosurgeon Dr. Steven Ojemann performed DBS procedures on both sides of his brain in November and December of that year. Baumgartner, who is his neurologist, also informed Engelkemier about the sleep study, and he agreed to participate.

Today, Engelkemier said he is still working to improve his shuffling gait and control involuntary muscle movement by fine-tuning his DBS programming. His sleep, however, has improved significantly. He said he now regularly gets six to seven solid hours of sleep and no longer stays awake after a trip to the bathroom. The changes have improved his quality of life, Engelkemier added.

I used to be sure to get a nap every day, he said. Ive very seldom needed one since DBS. He works to stay active by riding his bike, playing golf and walking. He participates weekly in tai chi with other PD patients and wants to try paddle boarding.

Of course, Engelkemiers experience doesnt itself establish that DBS improves sleep or other non-motor symptoms in PD patients. But Baumgartner said he hopes his study can increase understanding about possible links between the two and perhaps open the door to more research.

For example, he said, researchers and clinicians might learn how to directly intervene with targeted DBS that stimulates specific areas of the brain involved in sleep. That could lead to improvements not only in sleep but also mood and cognition, Baumgartner said.

Were learning more and more how critical sleep is for the brain, both in adults with and without Parkinsons disease and other neurological conditions, he said. Thats true for kids and teenagers too.

For more information on the study, contact Lisa Hirt,[emailprotected].

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Google rebounds from unprecedented drop in ad revenue with a resurgence that pushes stock higher – ABC News

Google snapped out of an unprecedented advertising slump during its latest quarter, signaling a return to growth cycle needed to fuel investments in artificial intelligence technology that expected to reshape the competitive landscape

By

MICHAEL LIEDTKE AP Technology Writer

July 25, 2023, 4:29 PM ET

4 min read

SAN FRANCISCO -- Google snapped out of an unprecedented advertising slump during its latest quarter, signaling a return to growth cycle needed to fuel investments in artificial intelligence technology that expected to reshape the competitive landscape.

The results for the April-June period released Tuesday by Google's corporate parent, Alphabet Inc., reversed a financial downswing that had raised fears Google was losing its financial steam at the same time advances in artificial intelligence, or AI, threatened to undercut the dominant search engine that powers its digital ad empire.

But after Googles ad revenue suffered year-over-year declines in consecutive quarters for the first time in its history, ad sales rose 3% from a year ago to $58.1 billion during the second quarter. That was better than analysts had been anticipating, according to FactSet Research.

Those gains helped lift Alphabets total revenue for the period by 7% from last year to $74.6 billion. The company posted a profit of $18.4 billion, or $1.44 per share, a 15% increase from the same time last year. Both those numbers also surpassed the analyst estimates that steer investors.

The Mountain View, California, company also announced that Chief Financial Officer Ruth Porat will take on the newly created role of president and chief investment officer. Alphabet will seek a new CFO to take over a job that Porat, a former investment banker, has handled for the past eight years.

Alphabets stock price surged nearly 10% in Tuesdays extended trading after the results came out. The shares have climbed nearly 50% so far this year, with much of the gains since Google provided a deeper dive into its AI products and strategy during a May conference. That presentation helped alleviate concerns that Google is being outmaneuvered in a pivotal field of technology by Microsoft, which is backing and deploying some of the breakthroughs made by Open AI and its popular chatbot, ChatGPT.

We are in a period of incredible innovation in search, Alphabet CEO Sundar Pichai said Tuesday during a conference call he conducted from London, where he said he was visiting with the DeepMind division overseeing the companys AI efforts.

The brewing battle for AI supremacy is expected to require billions of dollars in investments in the years to come money that Alphabet should be able to get from Googles advertising machine, as long as it can continue the steady growth of the past 20 years. But Google has recently been facing more daunting challenges, not only from the ChatGPT-like technology that Microsoft has been embedding in its Bing search engine, but also from Amazon in shopping, and TikTok and Reddit in hot topics.

Insider Intelligence analyst Evelyn Mitchell-Wolf said the past quarter showed Google remains on the high ground. All it has to do now is keep increasing revenues while executing its carefully-laid plans to lead advertisers and consumers into the AI-powered future without any catastrophes.

After YouTube saw its ad sales fall year-over-over in three straight quarters as TikToks audience swelled, Googles popular video site also bounced back with a 4% increase in ad revenue from last year. Meanwhile, the Google Cloud division that provides the behind-the-scenes technology for a wide swath of websites posted a 28% increase in revenue from last year. The Cloud division also posted its second consecutive profitable quarter in a development that has pleased investors.

Porat's promotion signaled Alphabet is looking to pare the losses in its long-unprofitable Other Bets division that includes self-driving car pioneer Waymo and other far-flung projects working on technology expected to take many years to yield a return. When she takes on her new role as Alphabet's president, Porat will oversee the Other Bets portfolio as part of a commitment to drive financial discipline and returns for shareholders, while spearheading investment to create sustainable, long-term value, Pichai said in a statement.

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Innovation Party Weekly. Another AI-era week with lots to talk | by … – Medium

The website of X.ai is now live with a landing page summing up the mission, vision, and leading team.

It will be the super app, the everything app, old Twitter turned into an AI platform that questions understanding the universe. X is an answer to the OpenAI for-profit Microsoft platform of GPT or the open-source AI platform of Facebook, Llama, which Facebook is opening for developers.

X is the official announcement of an unannounced war between the tech giants: Google, Elon Musk, Facebook, and Microsoft.

X.com now redirects to Twitter.

Lets not forget Tesla is one of the major AI companies in the world which is about to finally tackle the auto-pilot problem. Tesla has 200 engineers on the software side, 100 on the chip design side and 300 on the labeling side of AI development.

Elon Musk is also the only international person and Tesla is the only company in China who is really welcome to work in China. China has the only super app, WeChat, currently available, and Elon Musk praises WeChat often.

Tesla GigaShanghai was Tom Zhu and he is now the president of Tesla GigaTexas thanks to his extraordinary skills in building and managing Tesla China. Zhu is seen by many as the next CEO of Tesla if Elon Musk needs to focus on another even larger company, which can be X.

This means that Chinas AI power wind is also behind Xs development.

We shall see in time if this will be one of the great Xs of Elon such as SpaceX, X.com, and his son X. If it works, it may also be everyones favorite letter.

In engineering, there is a saying to mention a great number of things and that is n number of things. For Elon Musk, x is everything or everything is x.

It may also be worth mentioning that Elon Musk is the biggest brand and power in the tech sphere now. So, hundreds of thousands of skilled people would get in line to work with him to create the vision, and millions of people would sign up for any service he launches almost instantly.

One more thing, Threads is dead already?

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AI Could Spark the Most Productive Decade Ever. Nvidia Could Benefit. – Barron’s

This article is from the free weekly Barrons Tech email newsletter. Sign up here to get it delivered directly to your inbox.

Smarter Siri. Hi everyone. Excitement over generative artificial intelligence has surged since OpenAIs release of ChatGPT. But I often wonder if AI is improving dramatically, why are our voice recognition assistants still so dumb?

Most people dont use Amazons (ticker: AMZN) Alexa or Apples (AAPL) Siri for anything beyond simple tasks like setting timers or getting a weather update. The two services rarely offer quality responses to factual queries.

But now there may be an app for that. In May, start-up Inflection AI released its Pi chatbot for iPhone. The name stands for personal intelligence. Its trying to become a more thoughtful and supportive AI companion. Users can interact with Pi through voice or text.

I recently started using Pi and was struck by how much better it is for fact-based questions than Siri. You can choose between six different AI voices, and it does a good job of carrying on a natural back-and-forth conversation. Pis answers are concise and informative. And the verbal interface seems like a natural evolution for large-language-model AI technology, compared to ChatGPTs text-based format. It is an experience that feels right out of a Star Trek-like future.

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Inflection AI has an impressive pedigree. The founders came from DeepMind, an AI company renowned for its expertise, which was acquired by Alphabet s (GOOGL) Google. Other employees previously worked at OpenAI and Meta Platforms (META). The company has top notch investors too. In June, Inflection AI announced it had raised a $1.3 billion funding round from Microsoft (MSFT), Nvidia (NVDA), Reid Hoffman, Bill Gates, and Eric Schmidt.

Barrons Tech recently spoke to Inflection AI co-founder and CEO Mustafa Suleyman. We discussed the state of AI technology, the competitive landscape for AI semiconductors, his recent meeting with President Joe Biden on AI regulation, and his optimistic vision for the future of AI.

[Inflection AI was among the seven leading AI companiesincluding Amazon , Meta Platforms, Microsoft, Google, and privately held firms OpenAI and Anthropicthat recently made voluntary commitments to responsibly develop AI.]

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Suleyman is a venture partner at Greylock Partners. Earlier in his career, he co-founded DeepMind. At Google, he was vice president of AI products & AI policy. His upcoming book The Coming Wave, about AIs societal ramifications, is scheduled for release on Sept. 5.

Here are highlights from our conversation with Suleyman:

Barrons: How was the AI summit at the White House last week?

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Suleyman: There was a great energy from the administration and from the companies. I think on both sides there was a sense this is unprecedented times. The pace of evolution is incredible. That justifies optimism and excitement, but at the same time its a moment for the precautionary principle.

What were the big takeaways?

The voluntary commitment [to test] these models [with internal and external teams] is a pretty significant development. If you think about the way it works in security today, most of the time when vulnerabilities are disclosed there is a 60 day window to share the exploits among the companies. Thats a cultural practice. There is no regulation around that. Its a norm.

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You can imagine a similar kind of culture evolving here [with AI companies] where frontier model development companies identify weaknesses in their model and they share those exploits with the other companies.

Thats a huge positive contribution to improving the probability of good outcomes. You can think about that happening in the context of bioweapons development, cyberweapons development, and nuclear and chemical weapons development. Theres plenty of types of conversation that we would not want to make it really easy for a bunch of people to suddenly get educated on how to do these harmful things.

Voice conversation seems to be the natural evolution for AI chatbots like Inflection AIs Pi. Whats your vision for Pi going forward?

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I think in the future everyone is going to have their own personal AI. Its going to have three types of capability. Its going to have an EQa personality of being kind and compassionate. Its going to have IQ. It will have knowledge with deep expertise in many different areas. And in time it will have AQ, an action quotient. Its going to be able to take actions on your behalf. Book things, buy things, plan or organize.

Thats going to be where these things end up with those three capabilities. And Pi, we think, is going to be one of the first to get to that user experience.

What kinds of innovations do you see in large-language model AI technology over the next couple years?

We are about to train models that are 10 times larger than the cutting edge GPT-4 and then 100 times larger than GPT-4. Thats what things look like over the next 18 months.

Thats going to be absolutely staggering. Its going to be eye-wateringly different. I think we can largely eliminate hallucinations [chatbots tendency to make up facts and information] in the next two years.

Nvidia is one of the financial backers of your company. So let me ask you: What is the state of the AI chip market and how defensible is Nvidias leadership position?

The GPU [graphics processing unit] market is dominated by Nvidia . Thats because they produce the best chipshands downby a long way. They also provide the best software in CUDA, which is Nvidias development software that sits on top of the chip.

Nvidia is in a very strong position. They have a lot of experience developing these chips after many generations. It takes a long time to de-bug these chips. We have the largest cluster in the world of Nvidia H100 chips today. That means we can train the biggest, the best, and fastest models.

AMD (AMD) is promising for inference, but not so much for training. Inference means serving the model. They have a larger memory on those chips, so you can serve bigger models. AMD hasnt figured out how to daisy chain the chips together well for big training jobs. Nvidia has thought about parallelization and [high bandwidth connection] networking [better].

What are your predictions on how AI can lead to prosperity? But also, on the flip side, how can it disrupt society?

I do think its going to be the most productive decade in the history of our species. Anyone who is a creator or an inventor is now going to have a compadre who gets their domain.

People who are trying to be productive are now going to have an aide that is going to turbocharge their productivity. Thats going to save people an insane amount of time. Its going to make us much more creative and inventive.

On the flip side, anyone who has an agenda to cause disruption, cause chaos, or spread misinformation, is also going to have the barriers of entry for their destabilization efforts lowered.

Technology tends to accelerate offense and defense at the same time. A knife can be used to cut tomatoes or to hurt somebody. Thats the challenge of the coming wave. Its about containment. How do nation states control the proliferation of very powerful technologies, which can ultimately be a threat to the existence of the nation state if they are left unchecked?

Thanks for your time, Mustafa.

Write to Tae Kim at tae.kim@barrons.com or follow him on Twitter at @firstadopter.

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Artificial Intelligence In Law Market Is Thriving Worldwide- AIBrain … – Glasgow West End Today

TheArtificial Intelligence In Lawreport is an in-depth examination of the global Artificial Intelligence In Laws general consumption structure, development trends, sales techniques, and top nations sales. The research looks at well-known providers in the global Artificial Intelligence In Law industry, as well as market segmentation, competition, and the macroeconomic climate. A complete Artificial Intelligence In Law analysis takes into account a number of aspects, including a countrys population and business cycles, as well as market-specific microeconomic consequences. The global market research also includes a specific competition landscape section to help you better understand the Artificial Intelligence In Law industry. This information can help stakeholders make educated decisions before investing.

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The report is classified into multiple sections which consider the competitive environment, latest market events, technological developments, countries and regional details related to the Artificial Intelligence In Law. The section that details the pandemic impact, the recovery strategies, and the post-pandemic market performance of each actor is also included in the report. The key opportunities that may potentially support the Artificial Intelligence In Law are identified in the report. The report specifically focuses on the near term opportunities and strategies to realize its full potential. The uncertainties that are crucial for the market players to understand are included in the Artificial Intelligence In Law report.

As a result of these issues, the Artificial Intelligence In Law industry has been hampered. Because of the industrys small number of important enterprises, the Artificial Intelligence In Law area is heavily targeted. Customers would benefit from this research since they would be informed about the current Artificial Intelligence In Law scenario. The most recent innovations, product news, product variants, and in-depth updates from industry specialists who have effectively leveraged Artificial Intelligence In Law position are all included in this research study. Many firms would benefit from Artificial Intelligence In Law research study in identifying and expanding their global demand. Micro and macro trends, important developments, and their usage and penetration across a wide variety of end-users are also included in the Artificial Intelligence In Law segment.

The market analysis done with statistical tools also helps to analyze many aspects that include the demand, supply, storage costs, maintenance, profit, sales, and production details of the market. Furthermore, the global Artificial Intelligence In Law research report provides the details about the Artificial Intelligence In Law share, import volume, export volume, and the gross margin of the companies.

Artificial Intelligence In Law Segmentation by Type:

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Speech Recognition Software, Document Capture and Automated Workflows, Redaction and Encryption, Others

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Table of Content:

1 Scope of the Report1.1 Market Introduction1.2 Research Objectives1.3 Years Considered1.4 Market Research Methodology1.5 Economic Indicators1.6 Currency Considered2 Executive Summary3 Global Artificial Intelligence In Law by Players4 Artificial Intelligence In Law by Regions4.1 Artificial Intelligence In Law Size by Regions4.2 Americas Artificial Intelligence In Law Size Growth4.3 APAC Artificial Intelligence In Law Size Growth4.4 Europe Artificial Intelligence In Law Size Growth4.5 Middle East & Africa Artificial Intelligence In Law Size Growth5 Americas6 APAC7 Europe8 Middle East & Africa9 Market Drivers, Challenges and Trends9.1 Market Drivers and Impact9.1.1 Growing Demand from Key Regions9.1.2 Growing Demand from Key Applications and Potential Industries9.2 Market Challenges and Impact9.3 Market Trends10 Global Artificial Intelligence In Law Forecast11 Key Players Analysis12 Research Findings and Conclusion

MR Accuracy Reports is the number one publisher in the world and have published more than 2 million reports across globe. Fortune 500 companies are working with us. Also helping small players to know the market and focusing on consulting.

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The Deepest Breath: A haunting dive into beauty and tragedy – Economic Times

Documentary, "The Deepest Breath", has captured the attention of viewers, creating waves on Twitter and positioning itself as one of the most exquisite, suspenseful, and heart-rending documentaries available on OTT platforms. Tweeters and critics alike have found themselves deeply moved by its portrayal of real-life events and its mesmerizing cinematography.The film serves as a poignant tribute to the renowned Irish diver, Stephen Keenan, whose life was tragically cut short while heroically saving his beloved Alessia Zecchini, an accomplished diver, during an expedition in Egypt.The opening scene alone has left audiences spellbound, with @glenodnl hailing it as a masterpiece of cinematographya haunting portrayal that lingers in the mind long after the credits roll. Viewers have taken to social media to express their emotional journeys, with @TheBrigitteEdit describing it as harrowing, devastating, and yet somehow life-affirming. The documentary has touched @DarciCanada so profoundly that she admits to holding her breath within just minutes of its commencement.Director Laura McGann has crafted a mesmerizing experience, spanning one hour and forty-eight minutes, that dives deep into the souls of its subjects. The tomatometer boasts an impressive 82% rating based on 45 reviews, with critics labeling it a compelling documentary akin to a gripping thriller. It artfully blends the heart-wrenching reality of a tragedy with stunning footage of the ocean's mysterious depths.Comparisons have been drawn with other acclaimed extreme sports documentaries, such as "Free Solo" and "Riding Giants," drawing praise from Noel Murray of the Los Angeles Times. He commends "The Deepest Breath" as an intense and captivating film that will resonate with fans of the genre.However, not all reviews have been without reservations. Natalia Winkelman of the New York Times believes that the film falls short in exploring the personal lives of its characters, leaving much to be desired in understanding their inner worlds and motivations.

Yet, despite differing opinions, the documentary's ethereal underwater cinematography remains a common point of admiration. Critics and viewers alike have praised its ability to create an immersive and nerve-shredding experience, especially when juxtaposed with the real-life tragedy that unfolds in the dark, mysterious realms of the ocean.

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Protect AI raises $35M to expand its AI and machine learning security platform – VentureBeat

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Protect AI, an AI and machine learning (ML) security company, announced it has successfully raised $35 million in a series A funding round. Evolution Equity Partners led the round and saw participation fromSalesforce Venturesand existing investors Acrew Capital, boldstart ventures, Knollwood Capital and Pelion Ventures.

Founded by Ian Swanson, who previously led Amazon Web Services worldwide AI and ML business, the company aims to strengthen ML systems and AI applications against security vulnerabilities, data breaches and emerging threats.

The AI/ML security challenge has become increasingly complex for companies striving to maintain comprehensive inventories of assets and elements in their ML systems. The rapid growth of supply chain assets, such as foundational models and external third-party training datasets, amplifies this difficulty.

These security challenges expose organizations to risks around regulatory compliance, PII leakages, data manipulation and model poisoning.

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To address these concerns, Protect AI has developed a security platform, AI Radar, that provides AI developers, ML engineers and AppSec professionals real-time visibility, detection and management capabilities for their ML environments.

Machine learning models and AI applications are typically built using an assortment of open-source libraries, foundational models and third-party datasets. AI Radar creates an immutable record to track all these components used in an ML model or AI application in the form of a machine learning bill of materials (MLBOM), Ian Swanson, CEO and cofounder of Protect AI, told VentureBeat. It then implements continuous security checks that can find and remediate vulnerabilities.

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Having secured total funding of $48.5 million to date, the company intends to use the newly acquired funds to scale sales and marketing efforts, enhance go-to-market activities, invest in research and development and strengthen customer success initiatives.

As part of the funding deal, Richard Seewald, founder and managing partner at Evolution Equity Partners, will join the Protect AI board of directors.

The company claims that traditional security tools lack the necessary visibility to monitor dynamic ML systems and data workflows, leaving organizations ill-equipped to detect threats and vulnerabilities in the ML supply chain.

To mitigate this concern, AI Radar incorporates continuously integrated security checks to safeguard ML environments against active data leakages, model vulnerabilities and other AI security risks.

The platform uses integrated model scanning tools for LLMs and other ML inference workloads to detect security policy violations, model vulnerabilities and malicious code injection attacks. Additionally, AI Radar can integrate with third-party AppSec and CI/CD orchestration tools and model robustness frameworks.

The company stated that the platforms visualization layer provides real-time insights into an ML systems attack surface. It also automatically generates and updates a secure, dynamic MLBOM that tracks all components and dependencies within the ML system.

Protect AI emphasizes that this approach guarantees comprehensive visibility and auditability in the AI/ML supply chain. The system maintains immutable time-stamped records, capturing any policy violations and changes made.

AI Radar employs a code-first approach, allowing customers to enable their ML pipeline and CI/CD system to collect metadata during every pipeline execution. As a result, it creates an MLBOM containing comprehensive details about the data, model artifacts and code utilized in ML models and AI applications, explained Protect AIs Swanson. Each time the pipeline runs, a version of the MLBOM is captured, enabling real-time querying and implementation of policies to assess vulnerabilities, PII leakages, model poisoning, infrastructure risks and regulatory compliance.

Regarding the platforms MLBOM compared to a traditional software bill of materials (SBOM), Swanson highlighted that while an SBOM constitutes a complete inventory of a codebase, an MLBOM encompasses a comprehensive inventory of data, model artifacts and code.

The components of an MLBOM can include the data that was used in training, testing and validating an ML model, how the model was tuned, the features in the model, model package formatting, OSS supply chain artifacts and much more, explained Swanson. Unlike SBOM, our platform provides a list of all components and dependencies in an ML system so that users have full provenance of their AI/ML models.

Swanson pointed out that numerous large enterprises use multiple ML software vendors such as Amazon Sagemaker, Azure Machine Learning and Dataiku resulting in various configurations of their ML pipelines.

In contrast, he highlighted that AI Radar remains vendor-agnostic and seamlessly integrates all these diverse ML systems, creating a unified abstraction or single pane of glass. Through this, customers can readily access crucial information about any ML models location and origin and the data and components employed in its creation.

Swanson said that the platform also aggregates metadata on users machine learning usage and workloads across all organizational environments.

The metadata collected can be used to create policies, deliver model BoMs (bills of materials) to stakeholders, and to identify the impact and remediate risk of any component in your ML ecosystem over every platform in use, he told VentureBeat. The solution dashboards user roles/permissions that bridge the gap between ML builder teams and app security professionals.

Swanson told VentureBeat that the company plans to maintain R&D investment in three crucial areas: enhancing AI Radars capabilities, expanding research to identify and report additional critical vulnerabilities in the ML supply chain of both open-source and vendor offerings, and furthering investments in the companys open-source projectsNB DefenseandRebuff AI.

A successful AI deployment, he pointe dout, can swiftly enhance company value through innovation, improved customer experience and increased efficiency.Hence, safeguarding AI in proportion to the value it generates becomes paramount.

We aim to educate the industry about the distinctions between typical application security and security of ML systems and AI applications. Simultaneously, we deliver easy-to-deploy solutions that ensure the security of the entire ML development lifecycle, said Swanson. Our focus lies in providing practical threat solutions, and we have introduced the industrys first ML bill of materials (MLBOM) to identify and address risks in the ML supply chain.

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Machine learning helps this company deliver a better online shopping experience – ZDNet

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When you're buying a bulky item like a sofa on the internet, the last thing you need is for the product to arrive and for it to look nothing like you expected -- that kind of error is costly and frustrating not only for the person buying the sofa, but also for the company selling the item.

So, what if there was a way to use technology to make the buying experience more enjoyable and to reduce the risk of an unpleasant surprise on delivery day?

Also: This retailer is using RFID tags to make in-person clothes shopping less frustrating

That's exactly what Wayfair is doing with the help of machine learning. The e-commerce company, which sells furniture and home goods online, is using a specially designed platform that's been built alongside technology specialist Snorkel AI.

Tulia Plumettaz, director of machine learning at Wayfair, says the platform is helping her company boost the quality of the online search experience it provides to consumers and to help ensure the sofa you receive looks like the sofa you ordered.

"We have these bulky items that are hard to transport," she says. "We want you to get inspired and feel confident that what you're going to get is what you're buying. And we want that to happen without you even touching the product."

Also: Is Temu legit? What to know about this shopping app before placing your first order

Delivering this kind of high-quality online experience is far from straightforward. Wayfair's site includes thousands of products with a huge number of potential variables, including size, color, and texture.

An added complication comes from the fact that the e-commerce company provides a platform for its suppliers to sell goods to customers. Plumettaz says Wayfair sometimes receives a limited amount of information on products from its suppliers, so providing detailed descriptions to customers can be tough.

It's at that point that Snorkel's platform plays a key role in providing enriched product information.

"We want suppliers to find that it's easy to work with us using our advanced technology. We want them to say, 'I gave Wayfair a picture, some information, and -- with not a lot of effort -- my item just started selling,'" Plumettaz says.

Plumettaz also says machine learning supports "fast-labeling operations" through a bespoke solution that's been developed through a design partnership.

Also: The best online thrifting apps

Snorkel already had its key product called Snorkel Flow, which is a data-centric AI platform for automated data labeling, integrated model training, and analysis.

But while Snorkel Flow is focused on text, Wayfair needed a solution that would support the programmatic labeling of images.

Plumettaz says the solution, which was developed over a twelve-month period by the two companies in combination, provides benefits for both companies: Wayfair gets to shape the technology it's using, and Snorkel gets a route into a new and fast-emerging market.

"We engaged together, and the result is a novel development that brings programmatic labeling into computer vision," says Plumettaz.

Also: Here's why everything on Temu is so cheap

Now, with the bespoke technology in place, Wayfair's team can label and re-label products quickly and effectively.

Rather than having to rely on humans to label up to 40 million products manually, automation deals with a lot of the heavy lifting before specialists within the business -- such as category managers -- ensure the right images are served to online shoppers, says Plumettaz. "With programmatic image labeling, we can match products in the catalogue to the items that customers are looking for as new trends emerge."

Machine learning is also a productivity enhancement -- with less time being spent on labeling images, employees can now focus on higher-value activities. "It's making what we do a lot more interesting," she says. "At Wayfair, our employees don't lack activities to do -- think about maintaining such a rich catalogue. So, now we can be more productive. It's helped make our lives easier and our work a lot more cost-effective."

While Wayfair has chosen to work with Snorkel, Plumettaz recognizes there are other technology players who continue to develop their own machine-learning solutions.

Also: I bought four brand-name tech gadgets on Temu for work. Here's how it went

She says each company has its own stack and, in such a fast-developing market, it's tough to know where machine learning goes next. Plumettaz advises other professionals who are looking at emerging technology to make early inroads and build strong partnerships.

"The field is moving so fast," she says. "Five years ago, it was a lot harder to integrate with a vendor in machine learning. Now, the hurdles to get a vendor approved are disappearing fast."

While machine learning can provide a big boon to customer and employee experiences, Plumettaz says professionals shouldn't let emerging technology work in isolation.

Left to its own devices, an automated system might start labeling products wrongly, leading to unhappy customers and what she refers to as "tremendous consequences".

"You can have an amazing model, but the noise that can come your way through a 1% error rate -- such as when a bulky item gets delivered to your home and it's wrong -- is huge."

The lesson for all business leaders is to ensure the human stays in the loop in what remains a nascent area of development.

Also: I bought some off-brand geeky stuff from Temu (and wasn't mad about it)

"It's a journey with a lot of these applications," she says. "Let's automate, but let's still have a layer that is checking that the automation is working."

Plumettaz provides more details about how that process works at Wayfair. "When we're not confident, we put the outputs in front of a human and get some feedback," she says. "I call it the path to automation. It's like a toddler; it's not yet an adult who can run. And that's the framework that we've been using for those kinds of applications."

Another lesson for professionals who are thinking about dabbling in machine learning is to focus on cross-organization integration and processes, especially in terms of how the technology is implemented, used, and exploited.

Plumettaz says the takeaway will be a familiar one for professionals who introduce new systems or systems: Don't just implement technology for the sake of it. "Partnering really closely with business owners and product owners is key," she says. "I think the blocker is less around the technology and more around thinking about machine learning as a business-value driver from the get-go."

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Meet FathomNet: An Open-Source Image Database That Uses Artificial Intelligence and Machine Learning Algorithms … – MarkTechPost

The ocean is changing at an unprecedented rate, making it challenging to maintain responsible stewardship while visually monitoring vast amounts of marine data. The amount and rate of the necessary data gathering are outpacing our capacity to process and analyze them quickly as the research community seeks baselines. The lack of data consistency, inadequate formatting, and the desire for significant, labeled datasets have all contributed to the limited success of recent advancements in machine learning, which have enabled quick and more complex visual data analysis.

In order to meet this requirement, several research institutions worked with MBARI to speed up ocean research by utilizing the capabilities of artificial intelligence and machine learning. One such outcome of this partnership is FathomNet, an open-source image database that employs cutting-edge data processing algorithms to standardize and aggregate carefully curated labeled data. The team believes that using artificial intelligence and machine learning will be the only way to speed up critical studies on ocean health and remove the bottleneck for processing underwater imagery. Details regarding the development process behind this new image database can be found in a recent research publication in Scientific Reports journal.

Machine learning has historically transformed the field of automated visual analysis, partly thanks to vast volumes of annotated data. When it comes to terrestrial applications, the benchmark datasets that machine learning and computer vision researchers swarm to are ImageNet and Microsoft COCO. To give researchers a rich, engaging standard for underwater visual analysis, the team created FathomNet. In order to establish a freely accessible, highly maintained underwater image training resource, FathomNet combines images and recordings from many different sources.

Research workers from MBARIs Video Lab carefully annotated data representing nearly 28,000 hours of deep-sea video and more than 1 million deep-sea photos that MBARI gathered during 35 years. About 8.2 million annotations documenting observations of animals, ecosystems, and objects are present in the video library of MBARI. This comprehensive dataset serves as a priceless tool for the institutes researchers and their international collaborations. Over 1,000 hours of video data were gathered by the Exploration Technology Lab of the National Geographic Society from various marine habitats and places across all ocean basins. These recordings have also been used in the cloud-based collaborative analysis platform developed by CVision AI and annotated by experts from the University of Hawaii and OceansTurn.

Additionally, in 2010, the National Oceanic and Atmospheric Administration (NOAA) Ocean Exploration team aboard the NOAA Ship Okeanos Explorer gathered video data using a dual remotely operated vehicle system. In order to annotate gathered videos more extensively, they started funding professional taxonomists in 2015. Initially, they crowdsourced annotations through volunteer participating scientists. A portion of MBARIs dataset, as well as materials from National Geographic and NOAA, are all included in FathomNet.

Since FathomNet is open source, other institutions can readily contribute to it and utilize it in place of more time- and resource-consuming, conventional methods for processing and analyzing visual data. Additionally, MBARI started a pilot initiative to use machine learning models trained on data from FathomNet to analyze video taken by remotely controlled underwater vehicles (ROVs). Using AI algorithms raised the labeling rate tenfold while reducing human effort by 81 percent. Machine-learning algorithms based on FathomNet data may revolutionize ocean exploration and monitoring. One such example includes using robotic vehicles equipped with cameras and enhanced machine learning algorithms for automatic search and monitoring of marine life and other underwater things.

With ongoing contributions, FathomNet currently has 84,454 images that reflect 175,875 localizations from 81 different collections for 2,243 concepts. The dataset will soon have more than 200 million observations after obtaining 1,000 independent observations for more than 200,000 animal species in various positions and imaging settings. Four years ago, the lack of annotated photos prevented machine learning from examining thousands of hours of ocean film. By unlocking discoveries and enabling tools that explorers, scientists, and the general public may utilize to quicken the pace of ocean research, FathomNet, however, turns this vision into a reality.

FathomNet is a fantastic illustration of how collaboration and community science may promote innovations in our understanding of the ocean. The team believes the dataset can aid in accelerating ocean research when understanding the ocean is more crucial than ever, using data from MBARI and the other collaborators as the foundation. The researchers also emphasize their desire for FathomNet to function as a community where ocean aficionados and explorers from all walks of life may share their knowledge and skills. This will act as a springboard to address problems with ocean visual data that otherwise would not have been achievable without widespread participation. In order to speed up the processing of visual data and create a sustainable and healthy ocean, FathomNet is constantly being improved to include more labeled data from the community.

This Article is written as a research summary article by Marktechpost Staff based on the research paper FathomNet: A global imagedatabase for enabling artifcial intelligence in the ocean. All Credit For This Research Goes To Researchers on This Project. Check out the paper, tool and reference article. Also,dont forget to joinour 26k+ ML SubReddit,Discord Channel,andEmail Newsletter, where we share the latest AI research news, cool AI projects, and more.

Khushboo Gupta is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Goa. She is passionate about the fields of Machine Learning, Natural Language Processing and Web Development. She enjoys learning more about the technical field by participating in several challenges.

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Meet FathomNet: An Open-Source Image Database That Uses Artificial Intelligence and Machine Learning Algorithms ... - MarkTechPost

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