The insult "bird brain" should probably be retired. Eurasian jays can pass the marshmallow test, some species have "culture", and even extinct avians like the dodo were probably smarter than we previously thought. Large birds called palaeognaths–the closest living relatives of dinosaurs–are considered more simple. However, a small study found that some large birds are also capable of innovation. They can solve a physical task in order to access food, according to a study published February 20 in the journal Scientific Reports.

As AI systems become more advanced and integrated into our lives, there has been a corresponding urgency to ensure they align with social values and norms, and that their benefits significantly outweigh any potential harms. In response to this imperative, legal and regulatory bodies globally are engaged in a concerted effort to develop comprehensive AI regulations. The increasing size, generality, opaqueness, and closed nature of present-day AI systems, however, pose significant challenges to effective regulation. Even when requirements can be articulated, it remains uncertain whether and how we can verify an AI system's compliance with these standards: A requirement that cannot be checked will not provide effective protection. If we believe that AI systems should be regulated, then AI systems must be designed to be regulatable.

Designing for the social, cultural, and ethical implications of ML are just as important as its technical advances.

A few years ago, there was a significant trend for CIOs to add new tasks to their job titles: in addition to information, it was usual to find tech chiefs taking on responsibility for technology, digital, and product. It was hard to speak with CIOs without hearing about the importance of customer experiences, whether for internal users or external clients, from the refining of home-working programs to the deployment of new e-commerce channels. They should now demonstrate how technical innovations ranging from artificial intelligence to virtual reality can help their company identify new markets and generate new revenue streams. Automation, on the other hand, should come with a warning: while these tools might help IT teams with their day-to-day tasks, someone must ensure that new applications fulfill high dependability and security criteria. Since increased automation will need IT professionals to spend more time engaging and overseeing, businesses should prioritize training and development to ensure that employees are prepared for a shift in responsibilities.

Microsoft and OpenAI's close relationship has taken another leap forward with the former gaining exclusive GPT-3 access. GPT-3 has been the talk of the AI town in recent months. OpenAI's innovation can help to create convincing articles and the company once deemed it too dangerous to release in a world where misinformation and fake news is already problematic. OpenAI never made GPT-3 publicly available but instead provided access to a limited number of trusted researchers. Microsoft announced today that it now has the exclusive rights to leverage GPT-3's "technical innovations to develop and deliver advanced AI solutions for our customers, as well as create new solutions that harness the amazing power of advanced natural language generation."

The next wave of technical innovation will be driven by businesses looking to provide more touchless experiences to their coronavirus-wary customers. If you had asked me a year ago where I thought the tech industry was headed, I probably would have answered that we are headed toward the age of "smart everything." Machine learning and artificial intelligence (AI) were really in vogue last year. It seemed that nearly every vendor was scrambling to include some sort of machine learning into their products. It reminded me of the way things were several years back when all the tech vendors were rushing to include cloud in their offerings.

The last 30 or so years in scholarly communications have been marked by a steady trend towards digital content creation and delivery; particularly for journals, the industry has now moved from print to online. However, online delivery may mask underlying print-based attitudes, as is evidenced from the continuing preference academics have for reading PDF rather than any other format. In this regard, the advent of machine learning, because it involves a semantic engagement with the content, may in the end be more far-reaching, in fact genuinely disruptive, as it looks to challenge existing business processes. Here, for the first time, is a tool that can identify the meaning contained within articles. Machine learning will have an impact throughout the publishing lifecycle, from discovery (the most obvious quick win) to authoring, classification and presentation.

In this paper we describe our ongoing work on SustaInno; an open-source search repository of innovations related to sustainability. SustaInno utilizes advanced information retrieval and text processing methods on technical innovations (initially patent data) to provide its users with practical, applicable, and detailed solutions to their sustainability related challenges. For example, problems like urban heat islands and rainwater waste are of major concern to most urban cities. Using our repository, decision makers can get quite in-depth solutions on practical approaches to address these and many other problems. The novelty of our work stems from three main factors: (1) such a repository does not exist,(2) it is focused on sustainability innovations which are of great importance for the creation of sustainable living environment, and (3) it provides a set of open-source tools and open-access datasets that could accelerate the dissemination of knowledge about sustainability.