Today I published a perspective paper on COVID-19. The paper is co-authored with members of the Cambridge Centre for AI in Medicine (which I recently founded and I am directing), and calls on governments and healthcare authorities to use proven AI and machine learning techniques and existing data to coordinate a response to the disease. If you'd like to ask me about the paper or discuss it further, please leave a question/comment below, and I'll get back to you. I've also provided a link to the full paper at the bottom of this post. Both the UK and the international community are still in the early stages of a crisis that will see an unbelievable amount of pressure put on social and healthcare infrastructure.

Today I published a perspective paper on COVID-19. The paper is co-authored with members of the Cambridge Centre for AI in Medicine (which I recently founded and I am directing), and calls on governments and healthcare authorities to use proven AI and machine learning techniques and existing data to coordinate a response to the disease. If you'd like to ask me about the paper or discuss it further, please leave a question/comment below, and I'll get back to you. I've also provided a link to the full paper at the bottom of this post. Both the UK and the international community are still in the early stages of a crisis that will see an unbelievable amount of pressure put on social and healthcare infrastructure.

Today I published a perspective paper on COVID-19. The paper is co-authored with members of the Cambridge Centre for AI in Medicine (which I recently founded and I am directing), and calls on governments and healthcare authorities to use proven AI and machine learning techniques and existing data to coordinate a response to the disease. If you'd like to ask me about the paper or discuss it further, please leave a question/comment below, and I'll get back to you. I've also provided a link to the full paper at the bottom of this post. Both the UK and the international community are still in the early stages of a crisis that will see an unbelievable amount of pressure put on social and healthcare infrastructure.

The next 10 years look just as topsy-turvy. Artificial intelligence and machine learning promise to change the competitive landscape for many companies. At the same time, talented professionals will continue to demand more from their jobs through increased calls for transparency around pay and fairness and more flexibility in work-life balance. It's a lot for companies to navigate, and they're struggling with it: an analysis by Korn Ferry of more than 150,000 leadership profiles shows that only 15% of business leaders today have the right blend of skills to be the leaders of tomorrow. But such disruption can be a boon to workers who are agile and forward thinking.

From its epicenter in China, the novel coronavirus has spread to infect 414,179 people and cause no less than 18,440 deaths in at least 160 countries across a three-month span from January 2020 till date. These figures are according to the World Health Organization (WHO) Situation report as of March 25th. Accompanying the tragic loss of life that the virus has caused is the impact to the global economy, which has reeled from the effects of the pandemic. Due to the lockdown measures imposed by several governments, economic activity has slowed around the world, and the Organization for Economic Cooperation and Development (OECD) has stated that the global economy could be hit by its worst growth rate since 2009. The OECD have alerted that the growth rate could be as slow as 2.4%, potentially dragging many countries into recession.

Human-assisted artificial intelligence platform Engineer.ai The startup with global presence including offices in Los Angeles, London, Delhi NCR, Mumbai, and Tokyo will use the capital to go deeper in engineering operations and drive customer acquisition. Besides, the Sachin Dev Duggal led company is also planning to expand its operations in the Asia Pacific region, especially India, South East Asia including China. Touted as the low code no code AI platform, Engineer.ai'Builder'

Engineer.ai, which uses Artificial Intelligence to help small and mid-sized organisations build their own bespoke software (custom or tailor-made software), has raised a Series A investment of $29.5 Mn, led by Lakestar and Jungle Ventures. The funding round also saw participation from DeepCore -- Softbank's AI-focussed investment fund. Founded by Sachin Dev Duggal and Saurabh Dhoot in 2012, Engineer.ai is a global company with split headquarters in Los Angeles and London, supported by offices in Delhi and Tokyo. The startup was formerly known as SD Squared and was rebranded to Engineer.ai. in June 2018. With over $24M in gross revenue and customers that include BBC, Virgin Group and the San Francisco Giants, Engineer.ai

The rise in the number of self-driving cars will lead to more binge drinking as people stop worrying about having to drive home from a pub or club, a study claims. Researchers from Curtin University, Australia, say if a group don't need to assign a designated driver due to having an autonomous car, they will likely drink more. The team found that more than a third of adults would increase the amount they usually drink if they could rely on a driverless car to get them home. Lead author Leon Booth said driverless cars would cut drink-driving rates but increase the amount of alcohol drunk by the population. The rise in the number of self-driving cars will lead to more binge drinking as people stop worrying about having to drive home from a pub or club, a study claims.

Washington – Researchers in the U.S. and China reported Monday they have developed an artificial intelligence tool that is able to accurately predict which newly infected patients with the novel coronavirus go on to develop severe lung disease. Once deployed, the algorithm could assist doctors in making choices about where to prioritize care in resource-stretched health care systems, said Megan Coffee, a physician and professor at New York University's Grossman School of Medicine who co-authored a paper on the finding in the journal Computers, Materials & Continua. The tool discovered several surprising indicators that were most strongly predictive of who went on to develop so-called acute respiratory disease syndrome (ARDS), a severe complication of the COVID-19 illness that fills the lungs with fluid and kills around 50 percent of coronavirus patients who get it. The team applied a machine learning algorithm to data from 53 coronavirus patients across two hospitals in Wenzhou, China, finding that changes in three features – levels of the liver enzyme alanine aminotransferase (ALT), reported body aches, and hemoglobin levels – were most accurately predictive of subsequent, severe disease. Using this information along with other factors, the tool was able to predict risk of ARDS with up to 80 percent accuracy.

The primary source of bias in machine learning is not in the algorithms deployed, but rather the data used as input to build the predictive models. In this talk we will discuss why this is a huge problem and what to do about it. Different sources of bias will be identified along with possible solutions for remedying the situation when deploying machine learning. We will also speak about the importance of transparency when using machine learning to predict outcomes that impact critical decisions. Stephen Brobst is the Chief Technology Officer for Teradata Corporation.