Who is behind it?: Two leading AI labs cited building AGI in their mission statements: OpenAI, founded in 2015, and DeepMind, a research lab founded in 2010 and acquired by Google in 2014. Still, the concept might have stayed on the margins if not for the same wealthy tech investors interested in the outer limits of AI. Musk invested in DeepMind and introduced the company to Google co-founder Larry Page. Musk brought the concept of AGI to OpenAI's other co-founders, like CEO Sam Altman.

Embeddings are one of the most versatile techniques in machine learning, and a critical tool every ML engineer should have in their toolbelt. It’s a shame, then, that so few of us understand what they are and what they’re good for! The problem, perhaps, is that embeddings sound slightly abstract and esoteric: In machine learning, an embedding ...

"Meet AI" is a comic that deals with questions like; Is the existential threat from AI real? Why is there a gap between media coverage of advancements and the ground reality of research? Why do models misbehave in the real world and how do we go about solving this problem? How did we end up in an AI bubble and why is there such a gold rush mentality in ML scholarship right now?

Now, however, with artificial intelligence (AI) – essentially advanced analytical models – coming onto the market, cybersecurity actually has the edge. At present, vendors are doing far more than hackers with AI. Not that we can expect it to stay that way forever, but right now the good guys have the upper hand – and that gives the industry some time to prepare itself for the eventual rise of AI-enabled cybercriminals. The value of AI in this model is that it lets companies take large volumes of information and find clusters of similarity. This is always the focus of cybersecurity to a degree, but organisations are often unequipped to do so in sufficient depth because of time and resourcing constraints.

The embrace of artificial intelligence has come quickly in government. In May 2017, Congress established the bipartisan Congressional Artificial Intelligence Caucus, and members have since introduced numerous pieces of AI legislation. More recently, the administration launched the American AI Initiative through a February 2019 executive order, and the Department of Defense released its own strategy on how to incorporate AI into national security. As government use of AI evolves, agency leaders will look for pathways to capitalize on opportunities, and the workforce will need new technical and social skills to succeed in AI-augmented workplaces. A new report -- More Than Meets AI: Assessing the Impact of Artificial Intelligence on the Work of Government -- aims to assist in that effort.

MeetAI London and NeurotechX want to join efforts and bring together a selected panel of experts in diverse aspects of machine learning and neuroscience. An open discussion centred around how this two fields work together, the current achievements, and the future goals and limitations. Neuroscience and artificial intelligence are heavily related and both are living a golden age. Machine learning has been inspired by the nervous systems since its first steps. Terms such as neural networks or reinforcement learning have been borrowed from natural sciences and translated into silicon.

Glint CEO Jim Barnett has written a great piece on how Artificial Intelligence is changing the way HR adds value to the company. It's unreal how far technology and analytics have come in such a short time. I'm very excited about what Glint is doing for its customers.