Availability of challenging benchmarks is the key to advancement of AI in a specific field.Since Legal Text is significantly different than normal English text, there is a need to create separate Natural Language Processing benchmarks for Indian Legal Text which are challenging and focus on tasks specific to Legal Systems. This will spur innovation in applications of Natural language Processing for Indian Legal Text and will benefit AI community and Legal fraternity. We review the existing work in this area and propose ideas to create new benchmarks for Indian Legal Natural Language Processing.

As AI is making its way into more companies, the board and senior executives need to mitigate the risk of their AI-based systems. One area of risk includes the reputational, regulatory and legal risks of AI-led ethical decisions. AI-based systems are often faced with making decisions that were not built into their models--decisions representing ethical dilemmas. For example, suppose a company builds an AI-based system to optimize the number of advertisements we see. In that case, the AI may encourage incendiary content that causes users to get angry and comment and post their own opinions.

The last 15 years have brought what Stanford University professor Erik Brynjolfsson calls the "productivity paradox." While there's been continuing advances in technology, such as artificial intelligence, automation, and teleconferencing tools, the U.S. and other countries have seen flagging productivity. But a productivity boom is coming soon, Brynjolfsson said at the recent EmTech Next conference hosted by MIT Technology Review. He pointed to advances in technology, particularly artificial intelligence programs that are as good as -- or better -- than humans at some things. Businesses should now focus on incorporating the technology into work processes and preparing employees, he said, and policymakers should make sure its adoption doesn't contribute to inequality.

Just days after GitHub announced its new Copilot tool, which generates complementary code for programmers' projects, web developer Kyle Peacock tweeted an oddity he had noticed. "I love to learn new things and build things," the algorithm wrote, when asked to generate an About Me page. While the About Me page was supposedly generated for a fake person, that link goes to the GitHub profile of David Celis, who The Verge can confirm is not a figment of Copilot's imagination. Celis is a coder and GitHub user with popular repositories, and even formerly worked at the company. "I'm not surprised that my public repositories are a part of the training data for Copilot," Celis told The Verge, adding that he was amused by the algorithm reciting his name.

Artificial Intelligence (AI) seems to be catching the attention of a large section of people, no doubt because of the infinite possibilities it offers. It assimilates, contributes as well as poses challenges to almost all disciplines including philosophy, cognitive science, economics, law, and the social sciences. AI and Machine Learning (ML) have a multiplier effect on increasing the efficiency of any system or industry. If used effectively, it can bring about incremental changes and transform the ecosystem of several sectors. However, before applying such technology, it is important to identify the problems and the challenges within each sector and develop the specific modalities on how the AI architecture will have the highest impact. In the justice delivery system, there are multiple spaces where the AI application can have a deep impact.

Emerging technologies meet both advocates and resistance as users weigh the potential benefits with the potential risks. To successfully implement new technologies, we must start small, in a few simplified forms, fitting a small number of use cases to establish proof of concept before scaling usage. Artificial intelligence is no exception, but with the added challenge of intruding into the cognitive sphere, which has always been the prerogative of humans. Only a small circle of specialists understand how this technology works -- therefore, more education to the broader public is needed as AI becomes more and more integrated into society. I recently connected with Josh Feast, CEO and cofounder of Boston-based AI company Cogito, to discuss the role of AI in the new era of work.

The fields of natural language processing and information retrieval saw a productive past two decades due largely to the emergence and worldwide adoption of two modern technologies: large-scale document indexing and storage facilities, of which perhaps the two most prominent brands are JSTOR and Google Books, and social networking sites that allow individual users to create and distribute various types of content, a considerable fraction of which exist in the form of texts (status updates, blog posts, and tweets). All these have led to a relentless growth in information-rich but unstructured collections of text data - referred to as corpora in natural language terminology - in terms of volume, velocity, and frequency such that manual approaches to document indexing and classification are quickly becoming obsolete. Outside the context of online archives, methods that enable automated classification and analysis of voluminous corpora would prove to be valuable technology. It has been applied to legal research [Ravi-kumar and Raghuveer, 2012] and for analyzing patterns behind railroad accidents [Williams and Betak, 2018]. In the commercial space, companies can take advantage of thousands of posts being contributed by users on a daily basis about their products and services on social media and review aggregator websites like Yelp and TripAdvisor.

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

The improvement of computers' capacities, advancements in algorithmic techniques, and the significant increase of available data have enabled the recent developments of Artificial Intelligence (AI) technology. One of its branches, called Machine Learning (ML), has shown strong capacities in mimicking characteristics attributed to human intelligence, such as vision, speech, and problem-solving. However, as previous technological revolutions suggest, their most significant impacts could be mostly expected on other sectors that were not traditional users of that technology. The agricultural sector is vital for African economies; improving yields, mitigating losses, and effective management of natural resources are crucial in a climate change era. Machine Learning is a technology with an added value in making predictions, hence the potential to reduce uncertainties and risk across sectors, in this case, the agricultural sector. The purpose of this paper is to contextualize and discuss barriers to ML-based solutions for African agriculture. In the second section, we provided an overview of ML technology from a historical and technical perspective and its main driving force. In the third section, we provided a brief review of the current use of ML in agriculture. Finally, in section 4, we discuss ML growing interest in Africa and the potential barriers to creating and using ML-based solutions in the agricultural sector.

A new biometrics privacy ordinance has taken effect across New York City, putting new limits on what businesses can do with the biometric data they collect on their customers. From Friday, businesses that collect biometric information -- most commonly in the form of facial recognition and fingerprints -- are required to conspicuously post notices and signs to customers at their doors explaining how their data will be collected. The ordinance applies to a wide range of businesses -- retailers, stores, restaurants and theaters, to name a few -- which are also barred from selling, sharing or otherwise profiting from the biometric information that they collect. The move will give New Yorkers -- and its millions of visitors each year -- greater protections over how their biometric data is collected and used, while also serving to dissuade businesses from using technology that critics say is discriminatory and often doesn't work. Businesses can face stiff penalties for violating the law, but can escape fines if they fix the violation quickly.