What I Learned From Looking at 200 Machine Learning Tools – Machine Learning Times – machine learning & data science news – The Predictive…

Originally published in Chip Huyen Blog, June 22, 2020

To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. The resources I used include:

After filtering out applications companies (e.g. companies that use ML to provide business analytics), tools that arent being actively developed, and tools that nobody uses, I got 202 tools. See the full list. Please let me know if there are tools you think I should include but arent on the list yet!

Disclaimer

This post consists of 6 parts:

I. OverviewII. The landscape over timeIII. The landscape is under-developedIV. Problems facing MLOpsV. Open source and open-coreVI. Conclusion

I. OVERVIEW

In one way to generalize the ML production flow that I agreed with, it consists of 4 steps:

I categorize the tools based on which step of the workflow that it supports. I dont include Project setup since it requires project management tools, not ML tools. This isnt always straightforward since one tool might help with more than one step. Their ambiguous descriptions dont make it any easier: we push the limits of data science, transforming AI projects into real-world business outcomes, allows data to move freely, like the air you breathe, and my personal favorite: we lived and breathed data science.

I put the tools that cover more than one step of the pipeline into the category that they are best known for. If theyre known for multiple categories, I put them in the All-in-one category. I also include the Infrastructure category to include companies that provide infrastructure for training and storage. Most of these are Cloud providers.

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What I Learned From Looking at 200 Machine Learning Tools - Machine Learning Times - machine learning & data science news - The Predictive...

Global Machine Learning in Medicine Market Scope and Price Analysis of Top Manufacturers Profiles 2019-2025 – Apsters News

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Global Machine Learning in Medicine Market Scope and Price Analysis of Top Manufacturers Profiles 2019-2025 - Apsters News

Machine Learning in Finance Market Checkout The Unexpected Future 2020-2026|Ignite, Yodlee, Trill AI, MindTitan – 3rd Watch News

HTF Market Intelligence added research publication document on Covid-19 Impact on Global Machine Learning in Finance Market breaking major business segments and highlighting wider level geographies to get deep dive analysis on market data. The study is a perfect balance bridging bothqualitative and quantitative information of Covid-19 Impact on Machine Learning in Finance market. The study provides valuable market size data for historical (Volume** & Value) from 2014 to 2018 which is estimated and forecasted till 2026*. Some are the key & emerging players that are part of coverage and have being profiled are Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance.

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2. Industry growth prospects and market share

According to HTF MI, major business segments sales figure will cross the $$ mark in 2020. Unlike classified segments popular in the industry i.e. by Type (, Supervised Learning, Unsupervised Learning, Semi Supervised Learning & Reinforced Leaning), by End-Users/Application (Banks, Securities Company & Others), the latest 2020 version is further broken down / narrowed to highlight new emerging twist of the industry. Covid-19 Impact on Global Machine Learning in Finance market will grow from $XX million in 2018 to reach $YY million by 2026, with a compound annual growth rate (CAGR) of xx%. The strongest growth is expected in some Asian countries opening new doors of opportunities, where CAGR is expected to be in double digits ##% from 2019 to 2026. This forecast of industry players hints good potential that will continue growth along with the industrys projected growth.

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Machine Learning in Finance Market Checkout The Unexpected Future 2020-2026|Ignite, Yodlee, Trill AI, MindTitan - 3rd Watch News

Machine learning finds use in creating sharper maps of ‘ecosystem’ lines in the ocean – Firstpost

EOSJul 01, 2020 14:54:08 IST

On land, its easy for us to see divisions between ecosystems: A rain forests fan palms and vines stand in stark relief to the cacti of a high desert. Without detailed data or scientific measurements, we can tell a distinct difference in the ecosystems flora and fauna.

But how do scientists draw those divisions in the ocean? A new paper proposes a tool to redraw the lines that define an oceans ecosystems, lines originally penned by the seagoing oceanographerAlan Longhurstin the 1990s. The paper uses unsupervised learning, a machine learning method, to analyze the complex interplay between plankton species and nutrient fluxes. As a result, the tool could give researchers a more flexible definition of ecosystem regions.

Using the tool on global modeling output suggests that the oceans surface has more than 100 different regions or as few as 12 if aggregated, simplifying the56 Longhurst regions. The research could complement ongoing efforts to improve fisheries management and satellite detection of shifting plankton under climate change. It could also direct researchers to more precise locations for field sampling.

A sea turtle in the aqua blue waters of Hawaii. Image: Rohit Tandon/Unsplash

Coccolithophores, diatoms, zooplankton, and other planktonic life-formsfloaton much of the oceans sunlit surface. Scientists monitor plankton with long-term sampling stations and peer at their colorsby satellitefrom above, but they dont have detailed maps of where plankton lives worldwide.

Models help fill the gaps in scientists knowledge, and the latest research relies on an ocean model to simulate where 51 types of plankton amass on the surface oceans worldwide. The latest research then applies the new classification tool, called the systematic aggregated ecoprovince (SAGE) method, to discern where neighborhoods of like-minded plankton and nutrients appear.

SAGE relies, in part, on a type of machine learning algorithm called unsupervised learning. The algorithms strength is that it searches for patterns unprompted by researchers.

To compare the tool to a simple example, if scientists told an algorithm to identify shapes in photographs like circles and squares, the researchers could supervise the process by telling the computer what a square and circle looked like before it began. But in unsupervised learning, the algorithm has no prior knowledge of shapes and will sift through many images to identify patterns of similar shapes itself.

Using an unsupervised approach gives SAGE the freedom to let patterns emerge that the scientists might not otherwise see.

While my human eyes cant see these different regions that stand out, the machine can, first author and physical oceanographerMaike Sonnewaldat Princeton University said. And thats where the power of this method comes in. This method could be used more broadly by geoscientists in other fields to make sense of nonlinear data, said Sonnewald.

A machine-learning technique developed at MIT combs through global ocean data to find commonalities between marine locations, based on how phytoplankton species interact with each other. Using this approach, researchers have determined that the ocean can be split into over 100 types of provinces, and 12 megaprovinces, that are distinct in their ecological makeup.

Applying SAGE to model data, the tool noted 115 distinct ecological provinces, which can then be boiled down into 12 overarching regions.

One region appears in the center of nutrient-poor ocean gyres, whereas other regions show productive ecosystems along the coast and equator.

You have regions that are kind of like the regions youd see on land, Sonnewald said.One area in the heart of a desert-like region of the ocean is characterized by very small cells. Theres just not a lot of plankton biomass. The region that includes Perus fertile coast, however, has a huge amount of stuff.

If scientists want more distinctions between communities, they can adjust the tool to see the full 115 regions. But having only 12 regions can be powerful too, said Sonnewald, because it demonstrates the similarities between the different [ocean] basins. The tool was published in arecent paperin the journalScience Advances.

OceanographerFrancois Ribaletat the University of Washington, who was not involved in the study, hopes to apply the tool to field data when he takes measurements on research cruises. He said identifying unique provinces gives scientists a hint of how ecosystems could react to changing ocean conditions.

If we identify that an organism is very sensitive to temperature, so then we can start to actually make some predictions, Ribalet said. Using the tool will help him tease out an ecosystems key drivers and how it may react to future ocean warming.

Jenessa Duncombe.Text 2020. AGU.

This story has been republished from Eosunder the Creative Commons 3.0 license.Read theoriginal story.

Find latest and upcoming tech gadgets online on Tech2 Gadgets. Get technology news, gadgets reviews & ratings. Popular gadgets including laptop, tablet and mobile specifications, features, prices, comparison.

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Machine learning finds use in creating sharper maps of 'ecosystem' lines in the ocean - Firstpost

HOW CRYPTOCURRENCY HAS INFLUENCED UK GAMING – Island Echo

If you are looking for some online casino action in the UK, youre in for a big surprise. Over the last two decades, most traditional casino brands have shifted to the digital landscape as more people in the country are now online. Its a smart strategy to target a wider market, and it has driven the impressive growth of the gambling industry.

One of the biggest trends powering online casinos is the use of cryptocurrency. For a country that has not been quick to embrace cryptocurrencies, theres a lot of excitement over crypto gambling as players look for better experiences.

This article looks at the impact of cryptocurrencies in the industry and the future of the trend.

Digital currencies have dominated financial debates since the launch of bitcoin over a decade ago. From bitcoin, Ethereum to Tether USDT, there are now over 1600 digital currencies offering alternative banking solutions to internet users.

For an industry thats dependent on fast pay-outs, the casino industry has been among the early adopters of cryptocurrencies as a form of payment. Most gambling operators offer multiple banking methods, but these are either slow, unsafe, or expensive.

The promise of efficiency and affordability offered by cryptocurrency has brought a revolutionary transformation to casinos. With many other industries now embracing cryptocurrencies, casino operators are at the forefront of this revolution.

A quick look at most casino complaint pages highlights the inefficiency of traditional banking methods. Clients have to wait for hours or days before cashing out. While deposits are fast, withdrawals at most casinos come with long delays.

Cryptocurrencies now promise instant pay-outs and this will have a huge impact on the industry. Many people who have suffered intermittent delays when withdrawing their winnings will love the idea of fast and seamless transactions. Already, bitcoin casinos have become a big hit with players in the country which highlights how influential this trend will become.

Players can now explore more games at online casinos because its cheaper to use cryptocurrency to play. For instance, you can now enjoy an excellent experience playing the Crazy Time live game at your favourite casino.

The two reels and one payline slot is simple and easy to play. It has a minimum bet is 0.10 mBTC, and the maximum is 400.00 mBTC. With an RTP of 96.08%, this is a good slot machine to boost your winnings. The slot by Evolution Gaming has four thrilling bonus rounds and juicy multipliers for more rewards.

While the internet offers a lot of convenience for users, there are grave concerns over security and privacy. For casinos that require a constant flow of funds, security and privacy issues have threatened the industry for a long time.

Cryptocurrencies have come as a godsend as they make transactions more secure and anonymous. Crypto transactions are encrypted and you dont have to provide personal data to deposit or withdraw money.

The fast and seamless exchange of addresses between parties offers protection, and this has attracted more people to try this banking option.

Cryptocurrency transactions are cheaper and this is one reason they have become popular at online casinos. You dont have to worry about losing your winnings through high charges, as most platforms offer free instant withdrawals.

Playing at online casinos as never been more exciting. With cryptocurrency gambling, you can try more games and enjoy a safe and secure playing experience. Whats more, it is now possible to enjoy all your winnings and play anonymously.

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HOW CRYPTOCURRENCY HAS INFLUENCED UK GAMING - Island Echo

Cryptocurrency Market Update: Why Bitcoin is at the beginning of a massive downfall? – FXStreet

Bitcoin price is literally playing with fire. The widely traded digital asset failed to sustain gains above $9,300 last week following a bounce from levels marginally under $9,000. This put the sellers at the helm of the price actions over the weekend.

BTC/USD continued with the downtrend under $9,200 to the extent that $9,100 gave in. At the time of writing, BTC/USD is trading at $9,075 while the trend is facing a strong bearish bias. The only thing currently saving the bulls is the shrinking volatility which is ensuring that there are no rapid price actions.

As mentioned, Bitcoin slipped under $9,000 for the second time in June last week. The first time, BTC/USD embraced the support at $8,900. The second was a shallow dip under $9,000 last week. However, another drop into the $9,000 range could be explosive towards $8,600. Note that, in May Bitcoin retested the support at $8,600 and based on the prevailing bearish picture $8,000 downward target looks quite conservative.

Related content:Cryptocurrency Market News: Bitcoin at the edge of a cliff, Ethereum and Ripple in the red

The losses in the market are not unique to Bitcoin but the entire market is in the red too. For example, Ethereum is currently seeking support at $220 after suffering rejection at $228. Ether had recovered from the weekend low at $215. However, the drab technical picture in the market suggests that losses could reach $200 in the near term.

Ripple price has not been left out and is affected by the selling pressure. XRP/USD is trading 0.5% lower and dealing with increased volatility and a bearish trend. The fourth-largest crypto has a market value of $0.1760 after correcting lower from $0.1769. Gains above $0.18 are needed to keep the price in an upward trajectory towards $0.19 and $0.20 respectively.

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Cryptocurrency Market Update: Why Bitcoin is at the beginning of a massive downfall? - FXStreet

Crypto Nation Is Growing: Switzerland Improves Environment for Blockchain and Cryptocurrency – Coin Idol

Jul 01, 2020 at 12:50 // News

Dubbed Crypto Nation for its friendly stance for innovations including blockchain and cryptocurrency, Switzerland confirmed the name once more. A new legal package passed by the Swiss Federal Council is going to remove all existing legal barriers for startups.

The newly amended law will be adjusted to create a friendly environment to the work of distributed registries while removing the flaws in the existing framework, particularly when it comes to transferring security tokens. As of now, it involves a lot of bureaucratic red tape.

If the package passes the final voting in the Council of States, it will stipulate the application of a distributed ledger for storing and transferring security tokens, thus facilitating the entire process for both users and service providers.

The only thing that remains unchanged is the taxation framework that exists in the country. According to the notice from the Federal Department of Finance, the present taxation framework has proved its efficiency when applied to cryptocurrency businesses.

Switzerland can boast of its friendly attitude to blockchain and cryptocurrency. The name Crypto Nation has been given to the country for a reason. The city of Zug solely accounted for about 600 blockchain startups, with its number growing steadily. No wonder that Zug was Called the Crypto Valley.

Earlier this year, the Swiss government registered the Bitcoin Association as a non-profit organization, thus fully legalizing and welcoming its educational and research efforts made in the field. According to the report by coinidol.com, a world blockchain news outlet, the Association is actively engaged in building up technical infrastructure for startups, setting up conferences for the exchange of best practices and spreading awareness about innovations.

Such a friendly position from the government also prompts traditional financial institutions to explore the potential for cooperation with crypto business. It started back in 2017 when the Falcon Group Bank offered its customers a special feature to manage blockchain assets such as bitcoin. Soon enough it was followed by Swissquote Group, the countrys leading online bank, who partnered with Bitstamp cryptocurrency exchange to offer bitcoin trading services.

A year later, in 2018, another Swiss bank, Maerki Baumann, announced permission for cryptocurrency-related operations, such as payments related to mining and rendering services. In January 2020, Arab Bank Switzerland, which is the first Arab financial institution having launched its operation within the country, launched trading and custody services using Bitcoin and Ethereum.

While the traditional finance sector, as well as governments of most countries, are cautious about blockchain and cryptocurrency, Switzerland is showing the lead of how traditions and innovations can co-exist to the benefit of the country and its people.

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Crypto Nation Is Growing: Switzerland Improves Environment for Blockchain and Cryptocurrency - Coin Idol

Binance gets the third cryptocurrency emoji on Twitter – Decrypt

In brief

Binance, the world's largest cryptocurrency spot exchange, now has its own emoji on Twitter. Anyone who tweets #BNB, #Binance, and several other hashtags will now see the Binance logo next to their tweet.

The Twitter emoji comes as the exchange plans to celebrate its third birthday on July 14. The exchange is giving out its own branded, non-fungible tokens to those who promote the exchange on social media.

Binance's new emoji was first spotted in the wild earlier today, when Binance CEO Changpeng Zhao simply tweeted #BNB. Now, the emoji is present in practically all of Binance's recent tweets. Binance Coin (BNB) is now one of just three cryptocurrencies with its own Twitter emoji.

Bitcoin (BTC) was the first cryptocurrency to get an emoji on Twitter, and was added to the platform back in February. Crypto.com Coin (CRO) was the second cryptocurrency to get its own branded emoji and was revealed last month as part of a competition.

Since the emoji went live, the price of Binance Coin has risen slightly from $15.37 to $15.75, up 2.4%. Not a huge pump, but a minor gain in an otherwise flat market.

Binance has not stated how much it spent on the emoji. But the Block's director of research Larry Cermak recently stated that any company that commits more than $50,000 in Twitter ad spend may be eligible for an emoji. And brands, such as PepsiCo and Anheuser-Busch, have reportedly paid as much as $1 million for branded emojis in the past.

The crypto community is now speculating which project will be next to launch its emoji, and both Bitcoin Cash (BCH) and Tron (TRX) are two particularly common picks.

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Binance gets the third cryptocurrency emoji on Twitter - Decrypt

What Defines Artificial Intelligence? The Complete WIRED …

Artificial intelligence is overhypedthere, we said it. Its also incredibly important.

Superintelligent algorithms arent about to take all the jobs or wipe out humanity. But software has gotten significantly smarter of late. Its why you can talk to your friends as an animated poop on the iPhone X using Apples Animoji, or ask your smart speaker to order more paper towels.

Tech companies heavy investments in AI are already changing our lives and gadgets, and laying the groundwork for a more AI-centric future.

The current boom in all things AI was catalyzed by breakthroughs in an area known as machine learning. It involves training computers to perform tasks based on examples, rather than by relying on programming by a human. A technique called deep learning has made this approach much more powerful. Just ask Lee Sedol, holder of 18 international titles at the complex game of Go. He got creamed by software called AlphaGo in 2016.

For most of us, the most obvious results of the improved powers of AI are neat new gadgets and experiences such as smart speakers, or being able to unlock your iPhone with your face. But AI is also poised to reinvent other areas of life. One is health care. Hospitals in India are testing software that checks images of a persons retina for signs of diabetic retinopathy, a condition frequently diagnosed too late to prevent vision loss. Machine learning is vital to projects in autonomous driving, where it allows a vehicle to make sense of its surroundings.

Theres evidence that AI can make us happier and healthier. But theres also reason for caution. Incidents in which algorithms picked up or amplified societal biases around race or gender show that an AI-enhanced future wont automatically be a better one.

The Beginnings of Artificial Intelligence

Artificial intelligence as we know it began as a vacation project. Dartmouth professor John McCarthy coined the term in the summer of 1956, when he invited a small group to spend a few weeks musing on how to make machines do things like use language. He had high hopes of a breakthrough toward human-level machines. We think that a significant advance can be made, he wrote with his co-organizers, if a carefully selected group of scientists work on it together for a summer.

Moments that Shaped AI

1956

The Dartmouth Summer Research Project on Artificial Intelligence coins the name of a new field concerned with making software smart like humans.

1965

Joseph Weizenbaum at MIT creates Eliza, the first chatbot, which poses as a psychotherapist.

1975

Meta-Dendral, a program developed at Stanford to interpret chemical analyses, makes the first discoveries by a computer to be published in a refereed journal.

1987

A Mercedes van fitted with two cameras and a bunch of computers drives itself 20 kilometers along a German highway at more than 55 mph, in an academic project led by engineer Ernst Dickmanns.

1997

IBMs computer Deep Blue defeats chess world champion Garry Kasparov.

2004

The Pentagon stages the Darpa Grand Challenge, a race for robot cars in the Mojave Desert that catalyzes the autonomous-car industry.

2012

Researchers in a niche field called deep learning spur new corporate interest in AI by showing their ideas can make speech and image recognition much more accurate.

2016

AlphaGo, created by Google unit DeepMind, defeats a world champion player of the board game Go.

Those hopes were not met, and McCarthy later conceded that he had been overly optimistic. But the workshop helped researchers dreaming of intelligent machines coalesce into a proper academic field.

Early work often focused on solving fairly abstract problems in math and logic. But it wasnt long before AI started to show promising results on more human tasks. In the late 1950s Arthur Samuel created programs that learned to play checkers. In 1962 one scored a win over a master at the game. In 1967 a program called Dendral showed it could replicate the way chemists interpreted mass-spectrometry data on the makeup of chemical samples.

As the field of AI developed, so did different strategies for making smarter machines. Some researchers tried to distill human knowledge into code or come up with rules for tasks like understanding language. Others were inspired by the importance of learning to human and animal intelligence. They built systems that could get better at a task over time, perhaps by simulating evolution or by learning from example data. The field hit milestone after milestone, as computers mastered more tasks that could previously be done only by people.

Deep learning, the rocket fuel of the current AI boom, is a revival of one of the oldest ideas in AI. The technique involves passing data through webs of math loosely inspired by how brain cells work, known as artificial neural networks. As a network processes training data, connections between the parts of the network adjust, building up an ability to interpret future data.

Artificial neural networks became an established idea in AI not long after the Dartmouth workshop. The room-filling Perceptron Mark 1 from 1958, for example, learned to distinguish different geometric shapes, and got written up in The New York Times as the Embryo of Computer Designed to Read and Grow Wiser. But neural networks tumbled from favor after an influential 1969 book co-authored by MITs Marvin Minsky suggested they couldnt be very powerful.

Not everyone was convinced, and some researchers kept the technique alive over the decades. They were vindicated in 2012, when a series of experiments showed that neural networks fueled with large piles of data and powerful computer chips could give machines new powers of perception.

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What Defines Artificial Intelligence? The Complete WIRED ...

Increasing Transparency at the National Security Commission on Artificial Intelligence – Lawfare

In 2018, Congress established the National Security Commission on Artificial Intelligence (NSCAI)a temporary, independent body tasked with reviewing the national security implications of artificial intelligence (AI). But two years later, the commissions activities remain little known to the public. Critics have charged that the commission has conducted activities of interest to the public outside of the public eye, only acknowledging that meetings occurred after the fact and offering few details on evolving commission decision-making. As one commentator remarked, Companies or members of the public interested in learning how the Commission is studying AI are left only with the knowledge that appointed people met to discuss these very topics, did so, and are not yet releasing any information about their recommendations.

That perceived lack of transparency may soon change. In June, the U.S. District Court for the District of Columbia handed down its decision in Electronic Privacy Information Center v. National Security Commission on Artificial Intelligence, holding that Congress compelled the NSCAI to comply with the Federal Advisory Committee Act (FACA). Under FACA, the commission must hold open meetings and proactively provide records and other materials to the public. This decision follows a ruling from December 2019, holding that the NSCAI must also provide historical documents upon request under the Freedom of Information Act (FOIA). As a result of these decisions, the public is likely to gain increased access to and insight into the once-opaque operations of the commission.

Lawmakers established the NSCAI in the John S. McCain National Defense Authorization Act (NDAA) for fiscal 2019 1051, which tasked the commission with consider[ing] the methods and means necessary to advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States. The commissions purview includes an array of issues related to the implications and uses of artificial intelligence and machine learning for national security and defense, including U.S. competitiveness and leadership, research and development, ethics, and data standards.

The NSCAI is currently chaired by Eric Schmidt, the former executive chairman of Googles parent company, Alphabet. The commissions 15 membersappointed by a combination of Congress, the secretary of defense and the secretary of commercereceive classified and unclassified briefings, meet in working groups and engage with industry. They report their findings and recommendations to the president and Congress, including in an annual report.

The Electronic Privacy Information Center (EPIC), a research center focused on privacy and civil liberties issues in the digital age, submitted a request to the NSCAI in September 2019, seeking access to upcoming meetings and records prepared by the commission under FACA and FOIA. In the six-month period prior to the request, the NSCAI held more than a dozen meetings and received more than 100 briefings, according to EPIC. At the time it filed the lawsuit, EPIC noted that the commissions first major report was also one month overdue for release. When the commission did not comply with the requests under FOIA and FACA, EPIC brought suit under the two laws.

EPICs complaint alleged that the NSCAI had conducted its operations opaquely in its short lifespan. Since its establishment, the commission has operated almost entirely in secret with meetings behind closed doors[,] and has failed to publish or disclose any notices, agendas, minutes, or materials. If Congress had intended the NSCAI to comply with FOIA and FACA, such activity would not satisfy the statutes requirements. Given the potential implications of federal artificial intelligence decisions for privacy, cybersecurity, human rights, and algorithmic bias, EPIC argued that [p]ublic access to the records and meetings of the AI Commission is vital to ensure government transparency and democratic accountability. The complaint also noted the potential ramifications of commission activities for the government, private sector, and public, as well as the importance of artificial intelligence safeguards in the national security context due to limited public oversight. According to EPIC, increasing public participation would permit greater input into the development of national AI policy by those whose privacy and data security could potentially be affected.

The U.S. District Court for the District of Columbia addressed EPICs FOIA claim in a December 2019 decision. FOIA requires agencies to disclose their records to a party upon request, barring exemptions (including for information classified to protect national security). EPIC alleged that the NSCAI failed to uphold its obligations under FOIA to process FOIA requests in a timely fashion; to process EPICs FOIA requests in an expedited manner, in accordance with EPICs claims of urgency; and to make available for public inspection and copying its records, reports, transcripts, minutes, appendixes, working papers, drafts, studies, agenda, or other documents. The commission, which at the time did not have a FOIA processing mechanism in place or other pending FOIA requests, argued that it was not an agency subject to FOIA.

The courts inquiry centered on whether the NSCAI is an agency under FOIA. Comparing the language establishing the NSCAI with FOIAs definition of agency, the court held that the NSCAI is subject to FOIA. In his decision, District Judge Trevor McFadden noted that Congress could have hardly been clearer. As a result, since that time, the commission has had to produce historical documents in response to FOIA requests.

FACA, by contrast, applies forward-looking requirements specifically to federal advisory committees. These mandates include requiring committees to open meetings to the public and announce them in the Federal Register, and to make reports, transcripts and other commission materials publicly available. The measures aim to inform the public about and invite public engagement with the committees that provide expertise to the executive branch. EPIC alleged that the NSCAI violated FACA by failing to hold open meetings and provide notice of them, and by failing to make records available to the public. EPIC sought mandamus relief pursuant to the alleged FACA violations.

In its June decision, the district court ruled that FACA applies to the NSCAI. The commission had filed a motion to dismiss the FACA claims, arguing that it could not be subject to both FOIA and FACA. Since the court had previously held the NSCAI to be an agency for purposes of FOIA, the commission reasoned that it could not simultaneously be an advisory committee under FACA. McFadden disagreed. Invoking the Roman God Januss two facesone forward-looking and the other backward-facinghe wrote, [L]ike Janus, the Commission does indeed have two faces, and ... Congress obligated it to comply with FACA as well as FOIA. The court could not identify a conflict between the requirements of the two statutes, despite differences in their obligations and exceptions. Rather, it noted that if such conflicts arise, it will be incumbent on the parties and the Court to resolve any difficulties. The court dismissed additional claims under the Administrative Procedure Act (APA) for lack of subject matter jurisdiction, as it determined that the commission is not an agency under the APA definition.

The courts decision turned on whether the NSCAI is an advisory committee subject to FACA. The court determined that the statutory text of the 2019 NDAA establishing the NSCAI fit[s] the [FACA] definition of advisory committee like a glove. Furthermore, turning to the full text of the 2019 NDAA, the court noted that the law contains at least two instances in which it explicitly exempts a government body from FACA. The court read the 2019 NDAA as silent when FACA applies and explicit when FACA does not apply. Given Congresss silence on the applicability of FACA to the NSCAI in the 2019 NDAAand again in the 2020 NDAAthe court reasoned that Congress intended the NSCAI to be subject to FACA.

In determining the NSCAI to be subject to FACA, in addition to FOIA, the court has compelled the commission to adopt a more transparent operating posture going forward. Since the December 2019 decision on FOIA, the NSCAI has produced a number of historical records in response to FOIA requests. The recent ruling on FACA grounds requires the NSCAI to hold open meetings, post notice of meetings in advance and make documents publicly available. As a result, the commissions process of compiling findings and developing recommendations for government action related to artificial intelligence and machine learning will likely become more accessible to the public.

The two court decisions come in time to have a noticeable impact on the remaining term of the temporary commission. While the NSCAI was previously due to disband later in 2020, the NDAA for fiscal 2020 1735 extended the commissions lifespan by one year, to October 1, 2021. Citing federal budgetary timelines and the pace of AI development, the commission released its first set of recommendations in March 2020 and expressed its intent to publish additional recommendations on a quarterly basis thereafter. The commission is due to submit its final report to Congress by March 1, 2021. As the NSCAI prepares to enter its final year of operations and develop its closing recommendations, the public will have a clearer window into the commissions work.

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Increasing Transparency at the National Security Commission on Artificial Intelligence - Lawfare