If artificial intelligence is truly our fasting moving technology, the law has been lagging far behind. Addressing the emerging legal issues requires an understanding of the technology and how it works. In his Technology Law column, Peter Brown examines how AI functions and some of its legal implications.

Timnit Gebru was wary of being labelled an activist. As a young, black female computer scientist, Gebru – who was born and raised in Addis Ababa, Ethiopia, but now lives in the US – says she'd always been vocal about the lack of women and minorities in the datasets used to train algorithms. She calls them "the undersampled majority", quoting another rising star of the artificial intelligence (AI) world, Joy Buolamwini. But Gebru didn't want her advocacy to affect how she was perceived in her field. "I wanted to be known primarily as a tech researcher. I was very resistant to being pigeonholed as a black woman, doing black woman-y things."

The cybersecurity industry has been targeted by technology and business leaders as one of the top advanced use cases for artificial intelligence (AI) and machine learning (ML) in the enterprise today. According to the latest studies, AI technology in cybersecurity is poised to grow over 23% annually through the second half of the decade. That'll have the cybersecurity AI market growing from $8.8 billion last year to $38.2 billion by 2026. The question seasoned cybersecurity veterans are asking themselves right now is, "How much does AI really help security postures and security operations?" There's a ton of unbounded optimism from the vendor marketing and consultant types, but practitioners are still reserving a lot of judgment.

Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).

While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies. We then present empirical results in two different contexts --- online content recommendation and sustainable abalone fisheries --- to underscore the applicability of our approach to a wide range of practical decisions. Taken together, these results shed light on inherent trade-offs in using machine learning for decisions that impact social welfare.

To adhere to the stringent time and budget requirements of construction projects, contractors are utilizing prefabricated construction methods to expedite the construction process. Prefabricated construction methods require an adequate schedule and understanding by the contractors and constructors to be successful. The specificity of prefabricated construction often leads to inefficient scheduling and costly rework time. The designer, contractor, and constructors must have a strong understanding of the assembly process to experience the full benefits of the method. At the root of understanding the assembly process is visualizing how the process is intended to be performed. Currently, a virtual construction model is used to explain and better visualize the construction process. However, creating a virtual construction model is currently time consuming and requires experienced personnel. The proposed simulation of the virtual assembly will increase the automation of virtual construction modeling by implementing the data available in a building information modeling (BIM) model. This paper presents various factors (i.e., formalization of construction sequence based on the level of development (LOD)) that needs to be addressed for the development of automated virtual assembly. Two case studies are presented to demonstrate these factors.

The war of words between Britain and the EU has begun ahead of next month's trade talks. But as Britain sets its own course on everything from immigration to fishing, there is one area where the battle for influence is only just kicking off: the future regulation of artificial intelligence. As AI becomes a part of our everyday lives -- from facial recognition software to the use of "black-box" algorithms -- the need for regulation has become more apparent. But around the world, there is rigorous disagreement about how to do it. Last Wednesday, the EU set out its approach in a white paper, proposing regulations on AI in line with "European values, ethics and rules". It outlined a tough legal regime, including pre-vetting and human oversight, for high-risk AI applications in sectors such as medicine and a voluntary labelling scheme for the rest.

We have entered the era of Big Data, Cloud Computing and Artificial Intelligence (AI). It is currently in a swift development phase and continuously making technological advances. India has been a growing hub for business and ranks among the most attractive investment destinations for technology transactions in the world. In recent times, the country has focused on technology, realising that it is a key component of economic development. The government has been extensively promoting research, business incubators and parks, and has been improving on the Global Innovation Index position since 2016. This year the Ministry of Science and Technology has been allotted its largest budget till date by the Government of India.

WASHINGTON – The United States waged a series of precision airstrikes on Thursday against an Iran-backed militia in Iraq that it blamed for a major rocket attack a day earlier that killed two American troops and a 26-year-old British soldier. The U.S. strikes appeared limited in scope and narrowly tailored, targeting five weapons storage facilities used by Kataib Hezbollah militants -- including facilities used to store weaponry for past attacks on U.S.-led coalition troops, the Pentagon said. Iraq's military said in a statement that the U.S. airstrikes hit four locations in Iraq. The U.S. military did not estimate how many people in Iraq may have been killed in the strikes, which officials said were carried out by piloted aircraft. But there no was no indication of the kind of high-profile killings that President Donald Trump authorized in January, when the United States targeted a top Iranian general, Qassem Soleimani.

Mistrust in artificial intelligence (AI) continues to manifest itself as the emerging technology spreads to industries spanning the tech world, with cybersecurity being no exception. A report released today by WhiteHat Security has revealed while over half of surveyed organisations use artificial intelligence (AI) or machine learning in their security stack, nearly 60% are still more confident in cyberthreat findings verified by humans over AI. The research is based on a survey of 102 industry professionals at RSA Conference 2020. The survey also suggested 75% of respondents us application security tools as part of their security infrastructure, and 40% of these applications use a hybrid AI and human-based verification system. WhiteHat says the combined factors of advancing and growing security threats and the technology talent gap has meant the need for AI and machine learning tools in security protocols is essential.