Collaborating Authors


The Download: The grim spread of the Buffalo shooting video, and crypto's tough test

MIT Technology Review

Although Twitch took down the livestream within two minutes from the start of the attack, a recording of the video was swiftly posted on a site called Streamable. That video was viewed more than 3 million times before it was taken down, according to the New York Times. Links to the recording were shared across Facebook and Twitter, and another clip that purported to show the gunman firing at people in the supermarket was visible on Twitter more than four hours after being uploaded. Additionally, TikTok users shared search terms that would take viewers to the full video on Twitter, according to Washington Post reporter Taylor Lorenz. Although Twitch removed the livestream in less time than the 17 minutes it took Facebook to take down the live broadcast of the 2019 mosque shooting.

Devaluing Stocks With Adversarially Crafted Retweets


A joint research collaboration between US universities and IBM has formulated a proof-of-concept adversarial attack that's theoretically capable of causing stock market losses, simply by changing one word in a retweet of a Twitter post. In one experiment, the researchers were able to hobble the Stocknet prediction model with two methods: a manipulation attack and a concatenation attack. The attack surface for an adversarial attack on automated and machine learning stock prediction systems is that a growing number of them are relying on organic social media as predictors of performance; and that manipulating this'in-the-wild' data is a process that can, potentially, be reliably formulated. Besides Twitter, systems of this nature ingest data from Reddit, StockTwits, and Yahoo News, among others. The difference between Twitter and the other sources is that retweets are editable, even if the original tweets are not.

The metaverse space race will be visible from Wall Street


We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The past 12 months have taught us that the metaverse is regarded by many as the next major frontier for tech. The furor caused by Facebook's conspicuous rebranding to Meta has shown that the 21st century's biggest technological space race will be fought on the battlegrounds of Wall Street -- but the stock market has also helped to identify some of the forgotten players that will be integral to the mechanics of this brave new world. The race for the metaverse is a unique prospect for the 21st century, primarily because there is little understanding of what such a revolutionary mixed reality digital space will actually look like in practice -- or how businesses will be looking to capitalize on the new technology. So, who will succeed in profiting the most from the metaverse?

Forecasting Recessions With Scikit-Learn


It is no secret that everybody wants to predict recessions. Many economists and finance firms have attempted this with limited success, but by and large there are several well known leading indicators for recessions in the US economy. However, when presented to the general public these indicators are typically taken alone, and are not framed in a way that can give probability statements associated with an upcoming recession. In this project, I have taken several of those economic indicators and built a classification model to generate probabilistic statements. Here, the actual classification ('recession' or'no recession') is not as important as the probability of a recession, since this probability will be used to determine a basic portfolio scheme which I will describe later on.

Super-successful AI Investment Technologies Will Likely Never Be Publicly Available


It's tempting to see AI as a solution to building a super-success investment engine. After all, if AI can solve text-to-speech or self-driving cars or landing rockets vertically, couldn't an artificially intelligent investing engine with access to all stock market, economy, weather, and trends data vastly outpace human investors and guarantee massive returns? And won't we be able to simply ask Alexa to buy a stock that's going to triple in value in six months? Well, never say never, but it's unlikely. One is that investment AI engines are returning benefits right now, but not Everest-sized performance that will blow your financial socks off and make you fire your investment advisor.

SALT Launches Crypto Lending-As-A-Service; Announces Cion Digital as First Partner


SALT and Cion Digital announced a strategic partnership to bring SALT's crypto lending solutions to 5,000 auto dealerships in the US. The announcement marks the launch of SALT's Embedded Crypto Lending Service, which will enable financial service providers and fintech platforms to rapidly deploy crypto financing solutions. Having launched in 2016, SALT was the first platform to offer crypto-backed loans and has since been focused on optimizing its lending technology and servicing operations. Over the past few years, the Company has built a full stack loan management and risk platform that manages complex crypto loans at scale. With the launch of Embedded Crypto Lending, SALT is bringing this technology to other platforms, helping them further their mission to offer new and novel products to their customers.

Artificial Intelligence Drug RandD Market Scope and overview, To Develop with Increased Global Emphasis on Industrialization 2029


New Jersey (United States) – A2Z Market Research published new research on Global Artificial Intelligence Drug R&D covering the micro-level of analysis by competitors and key business segments (2022-2029). The Global Artificial Intelligence Drug R&D explores a comprehensive study on various segments like opportunities, size, development, innovation, sales, and overall growth of major players. The research is carried out on primary and secondary statistics sources and it consists of both qualitative and quantitative detailing. Various factors are responsible for the market's growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Artificial Intelligence Drug R&D market.

Infineon, BrainChip, Nvidia - Chip market facing the next wave


It was undoubtedly one of the outperformers in the first month of the current stock market year. The shares of the Australian company BrainChip Holdings, which also has subsidiaries in the US, India and France, almost quadrupled from AUD 0.71 to AUD 2.25 within one month. The announcement that Mercedes intends to develop systems based on BrainChip's Akida hardware and software caused a veritable buying panic. Among other things, the technology makes the "Hey, Mercedes" voice control in the EQXX five to ten times more efficient than conventional voice control. Since February, the stock has been in a strong consolidation phase, which is not unusual for such an innovative technology company.

3 Best Artificial Intelligence Stocks to Buy in April


Just about everyone knows artificial intelligence (AI) is taking hold in many parts of our lives. What most people are referring to when they say AI is actually machine learning (ML) -- the use of algorithms to mimic the way humans take in information and gradually learn to make more-accurate predictions. Some companies are doing it better than others. Three that are doing it well are trading at attractive valuations right now. Upstart ( UPST -2.06%), Microsoft ( MSFT 0.62%), and JPMorgan Chase ( JPM -0.30%) are all market leaders investing heavily in AI innovation.

Three former DeepMinders are developing A.I. to pick stocks and crypto


Three former DeepMind employees are trying to train a machine to spot and invest in company stocks and cryptocurrencies before they rise. Martin Schmid, Rudolf Kadlec and Matej Moravcik left Alphabet-owned DeepMind in January to set up EquiLibre Technologies, relocating from Edmonton in Canada to Prague in the Czech Republic in the process. The trio all used to work at IBM and in 2017 they developed an AI called DeepStack. It became the first AI capable of beating professional poker players at heads-up no-limit Texas hold'em poker. Now they're looking to apply some of these concepts to financial markets.