After releasing what may well have been the most comprehensive report on the State of AI in 2019, Air Street Capital and RAAIS founder Nathan Benaich and AI angel investor and UCL IIPP visiting professor Ian Hogarth are back for more. In the State of AI Report 2020, Benaich and Hogarth outdid themselves. While the structure and themes of the report remain mostly intact, its size has grown by nearly 30%. This is a lot, especially considering their 2019 AI report was already a 136 slide long journey on all things AI. The State of AI Report 2020 is 177 slides long, and it covers technology breakthroughs and their capabilities, supply, demand, and concentration of talent working in the field, large platforms, financing, and areas of application for AI-driven innovation today and tomorrow, special sections on the politics of AI, and predictions for AI.

Sweden is set to become a leader in the global Artificial Intelligence (AI) industry by prioritizing AI-driven initiatives and opportunities in the country. Artificial Intelligence (AI), referred to as intelligence demonstrated by machines, encompasses many technologies, such as, machine learning (ML), deep learning (DL), blockchain, etc. AI Sweden launched in February, 2019 (formerly, AI Innovation of Sweden) is an initiative by the Swedish Government's Ministry of Enterprise and Innovation to harness the opportunities offered by digital transformation in automation. The government is implementing this initiative in collaboration with the European Union and several partnering agencies, such as, Lantmäteriet, Vinnova, Knut and Alice Wallenberg Foundation, universities, and research institutions. The Wallenberg AI, Autonomous Systems and Software Program (WASP) was launched in 2015 but received an additional billion Swedisn kroner grant in 2018 from the Knut and Alice Wallenberg Foundation. This funding has expanded the AI Sweden initiative under the name of the Billion kroner program for AI research.

How much math knowledge do you need for machine learning and deep learning? Some people say not much. Both are correct, depending on what you want to achieve. There are plenty of programming libraries, code snippets, and pretrained models that can get help you integrate machine learning into your applications without having a deep knowledge of the underlying math functions. At some point in your exploration and mastering of artificial intelligence, you'll need to come to terms with the lengthy and complicated equations that adorn AI whitepapers and machine learning textbooks.

Louis Columbus in 10 Ways AI Is Going To Improve Fintech In 2020 stated that "Bottom Line: AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools." Since that time the Covid-19 crisis and tragedy arose. On the one hand Paul Clarke noted that UK fintech investment slumps by 40% amid Covid-19 crisis, whilst on the other Deloitte in Beyond COVID-19: New opportunities for Fintech companies note that "As the COVID-19 pandemic continues to create uncertainty, many fintechs are under stress on a number of fronts. But, as the broader economy shifts from "respond" to "recover", new opportunities may be created for some fintechs. A key question is how fintechs may leverage their unique assets and skills to seize new opportunities in the future. It could be an opportune time to think big and act boldly." Pavrita R considered the impact of Covid-19 and noted in 5 U.S. FinTech startups reimagining the healthcare industry notes that FinTech is undoubtedly shaping the face of the Health Care industry. "FinTech companies leverage powerful innovations blockchain, Artificial Intelligence, and Machine Learning to eliminate the inefficiencies and knowledge gaps endemic to most healthcare payment plans." The likes of Nigel Wilson (@nigewillson) and Brian Ahier (@ahier) have stressed the importance to apply AI to positive use cases such as preventative medicine and improved Health Care outcomes. McKinsey in an article entitled AI-bank of the future: Can banks meet the AI challenge? " The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1)." Source for image above: AI-bank of the future: Can banks meet the AI challenge? "While for many financial services firms, the use of AI is episodic and focused on specific use cases, an increasing number of banking leaders are taking a comprehensive approach to deploying advanced AI, and embedding it across the full lifecycle, from the front- to the back-office (Exhibit 2)" Source for image above: AI-bank of the future: Can banks meet the AI challenge? The Covid-19 crisis is a challenge both in terms of human health and also to the Fintech world.

Artificial neural networks are considered to be the most efficient Machine Learning techniques nowadays, with companies the likes of Google, IBM and Microsoft applying them in a myriad of ways. You've probably heard about self-driving cars or applications that create new songs, poems, images and even entire movie scripts! The interesting thing about this is that most of these were built using neural networks. Neural networks have been used for a while, but with the rise of Deep Learning, they came back stronger than ever and now are seen as the most advanced technology for data analysis. One of the biggest problems that I've seen in students that start learning about neural networks is the lack of easily understandable content.

Being one of the fastest developing deep learning aspects, TinyML has immense possibilities in areas where it is required to deploy a model that works on small and low power devices. Starting from imagery micro-satellite, tracking wildlife for conservation to detecting crop ailments, animal illnesses and predicting wildfires, TinyML comes with many possibilities. Not only it enables low-latency inference at edge devices consuming less power but also allows ML applications to run at edge intelligence. 'OK, Google' has been one of the renowned applications of TinyML, that works on everybody's smartphones. With such applications in hand, along with software frameworks like TensorFlow Lite for Microcontrollers, it has become extremely easy to deploy TinyML models.

Let me be upfront: I was the technical co-founder of an AI startup and it failed. PharmaForesight was an AI startup in the pharmaceutical business intelligence industry. "The rate of return for pharmaceutical companies on their R&D is currently below their cost of capital -- therefore it is becoming less profitable for pharmaceutical companies to invest in innovative drugs. To decide what clinical trials to conduct, the likelihood of approval is a crucial metric which is currently being calculated in a very subjective and biased way. Our AI algorithm can estimate this figure much more accurately, saving time, money and ultimately benefits patients."

Cogniac, a California-based provider of an enterprise-class Artificial Intelligence image and video analysis platform has now secured $10 million in Series B funding round, reports VentureBeat. Cogniac is founded by Chuck Myers. It utilizes AI technology to build inspection workflows, offering enterprise-class AI based deep learning solutions that automate visual inspection tasks like detecting, measuring, classifying, counting to human-level accuracy and beyond. Chuck Myers, CEO, Cogniac, said, "We are thrilled with our existing and new investors for leading this round and grateful for their support which will enable the business to continue to grow. We're also grateful for our incredible roster of customers who align with our objective of improving visual inspection. Their partnership has reinforced the value of our mission and encouraged our ambition to provide exceptional service and solutions."

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. If I told you to imagine something between a horse and a bird--say, a flying horse--would you need to see a concrete example? Such a creature does not exist, but nothing prevents us from using our imagination to create one: the Pegasus. The human mind has all kinds of mechanisms to create new concepts by combining abstract and concrete knowledge it has of the real world. We can imagine existing things that we might have never seen (a horse with a long neck--a giraffe), as well as things that do not exist in real life (a winged serpent that breathes fire--a dragon).