This week, we have launched the first in a series of reports on artificial intelligence (AI) and its potential impact in driving the digital transformation of biopharma. This overview report, Intelligent biopharma: Forging the links across the value chain, explores the challenges and opportunities in AI adoption and the potential ways that AI might impact the different segments of the biopharma value chain (see Figure 1).1 The pace and scale of medical and scientific innovation, together with increasing competition, lengthening R&D cycle times, shorter time in market, expiring patents, declining peak sales, pressure around reimbursement and mounting regulatory scrutiny are challenging the existing biopharma business and operating models. These challenges have also had a massively negative impact on the expected return on investment that large biopharma companies expect to achieve from their late-stage pipelines. Consequently, companies are looking to digital transformation as a key differentiator and essential part of their change management strategy. AI technologies are some of the most anticipated of these digital technologies.

A GPU is one of the most important components of modern-day artificial intelligence and deep learning architecture. Enterprises and developers are constantly on the lookout for tools that help them build and manage end-to-end data science and analytics pipelines seamlessly. RAPIDS is one such a tool incubated by NVIDIA based on the company's expert experience in hardware and data science. RAPIDS uses NVIDIA CUDA primitives for low-level compute optimisation, and lets developers use GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS's also helps with data preparation tasks for data science pipelines.

The original Minnesota Multiphasic Personality Inventory (MMPI) was published in 1940 and the second revised version--the MMPI-2--was published in 1989. It is the most widely used psychometric test for measuring adult psychopathology in the world. The MMPI-2 is used in mental health, medical and employment settings. The test developers Hathaway and McKinley used an empirical test construction technique to develop the MMPI. This involved basing the test scales (for example the hypochondriasis scale) on the actual test items that differentiate people with hypochondriasis from'normals'.

Researchers have successfully shrunk a giant language model to use in commercial applications. In October of last year, for example, Google released a model called BERT that passed a long-held reading-comprehension benchmark in the field. The larger version of the model had 340 million data parameters, and training it just one time through cost enough electricity to power a US household for 50 days. Four months later, OpenAI quickly topped it with its model GPT-2. The model demonstrated an impressive knack for constructing convincing prose; it also used 1.5 billion parameters. Now, MegatronLM, the latest and largest model from Nvidia, has 8.3 billion parameters.

The United States is blacklisting a group of Chinese tech companies that are developing facial recognition along with other artificial intelligence technologies that the U.S. says is used to repress China's Muslim minority organizations. A move Monday by the U.S. Commerce Department places the businesses on a so-called Entity List for acting in contrast to American foreign policy pursuits. The blacklist efficiently bars U.S. companies from offering technology to Chinese businesses with no government approval. The global providers of Video Surveillance Technology Hikvision and Dahua, are the blacklisted companies. Hikvision stated inside a declaration statement Monday it respects human rights and highly opposes the Trump administration's choice.

Move threatens Chinese companies' access to crucial components, relationships with U.S. firms

It is early days for the use of artificial intelligence (AI) and companies are still finding their way about where it best fits. Where AI should sit on an organisational chart remains something of a puzzle. But as more and more airlines, not to mention other travel suppliers, begin to use AI it is one that needs to be tackled. There are still many questions. For example, should data science be part of the IT department?

One day in 2017, Alexa went rogue. When Martin Josephson, who lives in London, came home from work, he heard his Amazon Echo Dot voice assistant spitting out fragmentary commands, seemingly based on his previous interactions with the device. It appeared to be regurgitating requests to book train tickets for journeys he had already taken and to record TV shows that he had already watched. Josephson had not said the wake word – "Alexa" – to activate it and nothing he said would stop it. It was, he says, "Kafkaesque". This was especially interesting because Josephson (not his real name) was a former Amazon employee.

WASHINGTON/BEIJING – The Trump administration on Tuesday slapped travel bans on Chinese officials involved in a massive crackdown on Uighurs and other Muslim minorities in its west. The State Department said it would not issue visas to Chinese government and Communist Party officials believed to be responsible for or complicit in mass detentions and abuses in Xinjiang province. It did not identify the officials or say how many were affected by the ban, which can also be applied to their immediate family members. U.S. lawmakers have specifically asked for action against Chen Quanguo, the Communist Party chief for Xinjiang and a member of the party's powerful Politburo, and U.S. officials have previously mentioned him when saying the Trump administration was considering sanctions against officials linked to China's crackdown on Muslims. Chen earlier led iron-fisted policies aimed at crushing dissent in Tibet and has gained a reputation within the party for his handling of minority groups.

Data (and Computer) Scientists have been working for a long time on improving the ability for algorithms to derive meaning from natural (human) languages -- whether they're trying to create a bot that responds to users questions on their website or determine whether people love or hate their brand on Twitter. The bad news is there is still a deep stack of concepts that you need to understand to tune your results. The good news is that with tools like BERT and ERNIE, getting good results from Natural Language Processing (NLP) is more accessible than ever -- even with modestly sized data sets and computing budgets. Plus, who wouldn't want to do NLP with the Sesame Street crew?! Let's start with a brief look at the history of the discipline. It's possible to break down the development of NLP systems into three broad phases: Deep learning has transformed the practice of NLP over the last decade.