This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion. Deep Reinforcement Learning will be considered in greater detail in part 3 of this series. For the purpose of this article I will consider AI to cover Machine Learning and Deep Learning. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task.

This is a story of how artificial intelligence, augmented reality, and virtual reality can transform the academic library into a hybrid space. The library becomes a network of digital connections between physical objects. A network that recommends resources based on personal needs, links print resources to multimedia, embeds interactive tools to enhance knowledge, turns the focus on making discoveries rather than looking for them, and so on. I'm going to talk to you today about information delivery, in the context of libraries, and what I see as the future of libraries. We are about to create a new experience.

Everybody poops--and after a few thousand years underground, these droppings often start to look the same. That stool-based similarity poses something of a puzzle for archaeologists investigating sites where dogs and humans once cohabited, as it isn't always easy to deduce which species left behind specific feces. But as a team of researchers writes in the journal PeerJ, a newly developed artificial intelligence system may end these troubles once and for all. Called corpoID--an homage to "coprolite," the formal term for fossilized feces--the program is able to distinguish the subtle differences between ancient samples of human and canine excrement based on DNA data alone, reports David Grimm for Science magazine. Applied to feces unearthed from sites around the world, the new method could help researchers unveil a trove of valuable information about a defecator's diet, health, and perhaps--if the excretion contains enough usable DNA--identity.

It feels as if a superhuman effort is needed to help ease the global pandemic killing so many. Artificial intelligence may have been hyped - but when it comes to medicine, it already has a proven track record. There is no shortage of companies trying to solve the dilemma. Oxford-based Exscientia, the first to put an AI-discovered drug into human trial, is trawling through 15,000 drugs held by the Scripps research institute, in California. And Healx, a Cambridge company set up by Viagra co-inventor Dr David Brown, has repurposed its AI system developed to find drugs for rare diseases.

Explainable artificial intelligence (XAI) is the attempt to make the finding of results of non-linearly programmed systems transparent to avoid so-called black-box processes. The main task of XAI is to make non-linear programmed systems transparent. It offers practical methods to explain AI models, which, for example, correspond to the regulation of the General Data Protection Regulation (GDPR). The following five methods are listed, which have to make AI models more transparent and understandable. Layer-wise Relevance Propagation (LRP) is a technique that brings such explainability and scales to potentially highly complex deep neural networks.

SodaStream, an Israeli manufacturer of fizzy drink devices, gained visibility in the U.S. and Europe as a healthy and environment friendly alternative to carbonated giants like Coca Cola. But soon after relocating from a controversial site in the occupied West Bank to a new facility in southern Israel, executives realised that the company is facing a new challenge: streamlining operations in order to stay competitive with low-cost manufacturer rivals from China while quenching a fast-growing thirst for its bubbly beverages. To rein in costs and make SodaStream's four manufacturing lines more efficient, executives decided to automate assembly lines with robots, computerise production, and connect all manufacturing processes under one control system. The multi-year project was aimed at boosting output to keep pace with 30 percent yearly sales surges, while utilising artificial intelligence, machine learning and cloud computing to get a better handle on optimising production. "We continued to grow rapidly and were packed with endless employees. The dining room was full. The production side was full. We knew that we wouldn't be able to allow ourselves to keep operating the same way… whether in terms of space, efficiency, or in terms of costs," said Kfir Suissa, chief operation officer at SodaStream, which was acquired by PepsiCo in 2018 for US$3.2 billion.

Trials have begun of a system using machine learning to predict the approaching demand across England for intensive care beds and ventilators for patients with Covid-19. NHS Digital said the Covid-19 Capacity Planning and Analysis System (CPAS) has been developed by its data scientists and researchers from the University of Cambridge, using data from Public Health England (PHE) and aimed at supporting hospitals in their planning. It has been built on the Cambridge Adjutorium machine learning engine, developed by a Cambridge team led by Professor Mihaela van de Schaar and which has already been used to obtain insights on cardiovascular disease and cystic fibrosis. It is using data collected by PHE's 19 Covid-19 Hospitalisation in England Surveillance System (CHESS). Alpha stage trials have begun at four hospitals.

Research in machine learning, mobile robotics, and autonomous driving is accelerated by the availability of high quality annotated data. To this end, we release the Audi Autonomous Driving Dataset (A2D2). Our dataset consists of simultaneously recorded images and 3D point clouds, together with 3D bounding boxes, semantic segmentation, instance segmentation, and data extracted from the automotive bus. Our sensor suite consists of six cameras and five LiDAR units, providing full 360 degree coverage. The recorded data is time synchronized and mutually registered.

London, UK 20 April 2020 – LabGenius Ltd ("LabGenius" or "the Company"), a biopharmaceutical company developing next-generation protein therapeutics using machine learning, today announces that it has appointed Dr Edwin Moses as Chairman of its Board of Directors. Edwin Moses was Chief Executive Officer of Ablynx NV until its agreed takeover by Sanofi for $4.8 billion in 2018. He was CEO of Ablynx for more than 12 years and built it from a small R&D-focused organisation into a 500-person commercial-ready business. The company developed a broad biologics pipeline including a wholly-owned product for a rare hematologic indication, which was approved for use in Europe in 2018 and the USA in 2019. While at Ablynx, Dr Moses led its Euronext Brussels listing, multiple successful private and public financings and its US NASDAQ listing in 2017 which raised $230M.

The graph represents a network of 2,097 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 19 April 2020 at 02:51 UTC. The requested start date was Sunday, 19 April 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 7-day, 7-hour, 29-minute period from Saturday, 11 April 2020 at 04:16 UTC to Saturday, 18 April 2020 at 11:46 UTC.