Tesla Inc. CEO Elon Musk famously served as the inspiration for Robert Downey Jr.'s depiction of billionaire genius Tony Stark in Marvel's "Iron Man" movies. Now he's trying to avoid a real-life Ultron moment. In the fictional Marvel universe, Stark originally designed Ultron, an artificial intelligence program, to protect Earth. But when it gained sentience, it turned on its creators and attempted to destroy humanity. Musk doesn't think it's the stuff of fiction.

The importance of artificial intelligence and machine learning (AI/ML) has not been lost on drug development companies. Recently, to help accelerate the discovery of therapies to treat COVID-19, several deals have been established to help deploy those tools. For example, Abcellera Biologics Inc., of Vancouver, British Columbia, and Eli Lilly and Co., of Indianapolis, agreed to co-develop antibody products for treating and preventing COVID-19. The collaboration will build on Abcellera's pandemic response platform, developed under the DARPA Pandemic Prevention Platform (P3) program, and Lilly's global capabilities for rapid development, manufacturing and distribution of therapeutic antibodies. Within one week of receiving a blood sample from one of the first U.S. patients who recovered from COVID-19, Abcellera screened more than 5 million immune cells looking for those that produced functional antibodies that helped the patient neutralize the virus and recover from the disease and identified more than 500 unique fully human antibody sequences.

Artificial intelligence deployments are no longer a risky bet for enterprises, but a "must-have" investment. Gartner has reported that 59% of organizations have deployed AI, with the average company expected to have 10 AI projects by the end of this year. It's obvious when a company is leading its industry or severely lagging, and both scenarios have clearly defined paths. But what about organizations whose AI is somewhere in the middle? What's holding them back, and how can they become a leader?

Natural Language Programming or NLP has enabled computers to interpret human language that has further opened doors to new innovation. Due to this very reason, the interest to learn more about the subject has increased in recent years, and as they say, books are the best place to gather knowledge from. But when it comes to books, the options are in millions, and it is hard to zero-in on one. Also, people often tend to look for the ones freely available in the form of eBook or PDFs, which are quite hard to come by. In this article, we present to you the top 7 NLP books one can get their hands on for free.

This article is based on the keynote given by Tony Jebara at TensorFlow World in Santa Clara, California, October 2019. You can watch the presentation here. Machine learning is at the heart of everything we do at Spotify. Especially on Spotify Home, where it enables us to personalize the user experience and provide billions of fans the opportunity to enjoy and be inspired by the artists on our platform. This is what makes Spotify unique.

The graph represents a network of 2,825 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 13 March 2020 at 05:32 UTC. The requested start date was Friday, 13 March 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 3-day, 13-hour, 2-minute period from Monday, 09 March 2020 at 10:57 UTC to Thursday, 12 March 2020 at 23:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

CAPE TOWN 17th March – AI Expo Africa is the continent's largest B2B trade-focused artificial intelligence (AI) business event. The expo, which is now in its third year, will be held at Century City Convention Centre in Cape Town on 3rd and 4th September 2020. Zindi, Learning Machines – which provides a number of services across machine learning and data science – and FinChatBot, which develops AI-powered chat bots for the financial services industry, will showcase their solutions at AI Expo Africa's Innovation Cafe. Other companies that will exhibit at AI Expo Africa's Innovation Cafe include data science specialists Ashanti AI and visitor management solutions provider WizzPass. AI Expo Africa Event Co-founder Dr Nick Bradshaw stated, "It's great to welcome back supporters like City of Cape Town, FinChatBot and Zindi, the latter launched at our 2018 event and have gone onto be a massive success. It's also great to introduce brand new supporters and companies like Learning Machines, WizzPass and Ashanti AI. Our goal is to promote the newest and best AI, RPA and Data Science companies and startups via our various media platforms to maximise brand exposure and generate new business for the growing 4IR community of suppliers that is emerging in Africa."

With "social distancing" the phrase of the week around the world, the time may have come for communities and governments to embrace drone delivery – not just for speed and convenience, but to protect themselves and vulnerable members. The World Economic Forum has published a blog post highlighting the contributions that drones have made in the fight against COVID-19. "3 Ways China is Using Drones to Fight Coronavirus" brings out the contributions made by drone delivery in an environment where limiting human-to-human contact is of paramount importance. In China, drone delivery company JD worked with government stakeholders to dramatically increase service areas to bring supplies to quarantined and isolated areas. "With the support of the local government, e-commerce company JD deployed its drone team. That team quickly conducted ground surveys, designed flight corridors, requested airspace access permission and conducted final flight tests. In just a few days, several drone delivery corridors were put in place replacing hours-long drives with a 2 km flight that could be completed in just 10 minutes," says the World Economic Forum's article.

"We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. And we can see whether there's a response in different parts of the field," said Nicolas Martin, assistant professor in the U of I Department of Crop Sciences and co-author of the study.

One important data type which includes time series, digital signals and any sequential observations is still mainly processed with rather standard mathematical and algorithmic routines. In this talk, we will review, what are the main sources of time series in the world, what are the "basic" algorithms and how exactly they might be improved and replaced with different neural network architectures. Apart from the models' details, we will also study the typical tasks that have to be solved while working with time series: classification, prediction, anomaly detection, simulation and others and exactly deep learning can be leveraged to solve them on the state-of-the-art level. Some previous experience with time series/signal processing is useful for getting the most out of this session, but not required. Alex Honchar is developing production-ready AI solutions for small and medium businesses for the last 5 years, giving public speeches in Europe and blogging about ML and AI recent advances.