The Chinese government has released a three-step blueprint, showing how it intends to become the leader in artificial intelligence development and deployment by 2030. The State Council, the chief administrative authority in China, published the plan last week. See Also: WeChat's director of user growth talks up new features for overseas clients China will look to "keep pace" with all other leading countries in AI by 2020. This means an AI industry worth $22 billion and $150 billion in related fields, such as self-driving. From there, the Chinese government will work to have all regulatory and legal framework set by 2025.

Global law firm Baker McKenzie has today announced the selection of eBrevia as its main AI tool of choice to be used on M&A and other transactional work for its global clients. The deal is a major coup for eBrevia as Baker McKenzie offices in Hong Kong, Singapore, Frankfurt, Munich, Berlin, Dusseldorf, Vienna, Toronto and Chicago will be tailoring eBrevia's machine learning capabilities to several practice groups and jurisdictions, and plans are already underway to extend the reach of the AI programme. Paul Rawlinson, global chair of Baker McKenzie said: 'Digitisation, Artificial intelligence and Machine Learning are all very real and the legal industry is right in the middle of the changes they will bring. Being truly innovative means listening to clients and understanding the challenges they face – so we can adapt and make sure the service we provide fits not just for today's technological challenges but also for the future.' Baker McKenzie's Erik Scheer, who chairs the Innovation Committee added: 'We are applying a broad range of Artificial Intelligence tools for due diligence, contracts, and e-discovery where these technologies can ensure market-leading efficiency.

Software may be eating the world, but AI is eating software. Funding in the sector continues to soar, as it was recently revealed that venture, corporate and seed investors have poured an estimated $3.6bn into AI and machine learning. This spells good news for bosses interested in boosting user experience. The AI revolution has now come to testing, ensuring startups like DiffBlue make headlines for using AI to disrupt IT and developer tasks considered too repetitive or time-consuming. So will the concept of testing become the latest casualty of the AI revolution?

Brazil forward Neymar said he needed a new challenge, as he joined Paris St-Germain from Barcelona for a world record fee of 222m euros (£200m). The 25-year-old won seven major trophies in his four seasons at the Nou Camp, including the Champions League once and La Liga twice. He said his father, Neymar Sr, wanted him to stay at Barcelona. "I have won all that a player can win," said Neymar, who will earn 45m euros (£40.7m) a year on a five-year deal. Writing on Instagram, he added: "I have conquered everything an athlete can conquer. I have lived unforgettable moments. But a player [me] needs challenges. "And for the second time in my life, I'll contradict my father." Neymar's transfer smashes the previous record set when Paul Pogba returned to Manchester United from Juventus for £89m in August 2016. His £782,000-a-week wages mean PSG's total outlay is £400m. The French side have called a news conference for 12:30 BST on Friday, and Neymar will be introduced to fans at PSG's first game of the season against Amiens at Parc des Princes on Saturday. PSG reached the last eight of the Champions League last season - knocked out by a Neymar-inspired Barcelona - and were beaten to the French title by Monaco. Neymar said he has joined "one of the most ambitious clubs in Europe". "Paris St-Germain's ambition attracted me to the club, along with the passion and the energy this brings," he added. "I feel ready to take the challenge.

NAGOYA – Toyota Motor Corp. and Mazda Motor Corp. are set to form a capital alliance to boost joint development of electric vehicles, sources close to the matter said Friday. Japan's largest carmaker is mulling taking a roughly 5 percent stake in Mazda, while Mazda may also invest in Toyota, the sources said. The two automakers are expected to announce the plan later in the day. Toyota is scheduled to release its earnings results for the April-June period on Friday afternoon. Global carmakers are facing growing costs to comply with stricter emission and other environmental regulations.

In some use cases, it is impossible for humans to replicate the performance of artificial intelligence. But businesses will need a lot of data for AI systems to be effective. Maybe you've seen an artificial intelligence (AI) system like Watson at work on "Jeopardy!" or have heard of its successes in medical diagnoses or other fields. Maybe you've only heard about other similar systems working through incredibly complex and large sets of data to produce results that even non-experts can understand, through visualizations or natural language. Either way, AI systems are impressing many on their march toward becoming essential business processes.

Artificial intelligence, more commonly known under AI acronym, has become a very hot topic these days. Forrester Research forecasts a 300 percent growth of AI investment this year. Toyota invests $100 million in fund for AI, UBS is trying to bring AI to its investment bank's operations, while VCs frivolously dream of replacing all of us with AI to cut costs. Some people even feel embarrassed because they have never used or implemented AI in their office or home. Obviously, many cybersecurity vendors leverage the term in an attempt to increase sales and impress their customers.

Robotic process automation (RPA) is a family of technologies designed to replicate human actions in order to complete a task or series of tasks. Unlike traditional programming constructs, RPA is designed to allow everyday workers to quickly deploy virtual counterparts to learn from and mimic the actions they use to accomplish routine tasks, therefore freeing the worker to handle more complex, and in some cases, more fulfilling work activities. According to a new report from Tractica, the market for RPA is developing rapidly, and the market intelligence firm forecasts that worldwide revenue in the sector will increase from $151 million in 2016 to more than $5.1 billion by 2025. The key industry sectors that are embracing RPA implementations include financial services & banking, utilities & telecommunications, retail & commercial, and healthcare & insurance. The market opportunity for RPA will be largest in Europe during the forecast period, with Asia Pacific and North America not far behind.

Imagine a world where the inanimate objects of the shipping industry could talk. What would today's ships tell us about the stress overweight containers place on their hulls? How would they describe the strain they are put under by certain weather conditions? And how would they express the real impact of slow steaming on their superstructure? It sounds the stuff of science fiction, but "talking" ships could be just a few years away.

One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured texts. Topic models are one popular machine-learning approach which infers the latent topical structure of a collection of documents. Despite their success --- in particular of its most widely used variant called Latent Dirichlet Allocation (LDA) --- and numerous applications in sociology, history, and linguistics, topic models are known to suffer from severe conceptual and practical problems, e.g. a lack of justification for the Bayesian priors, discrepancies with statistical properties of real texts, and the inability to properly choose the number of topics. Here, we approach the problem of identifying topical structures by representing text corpora as bipartite networks of documents and words and using methods from community detection in complex networks, in particular stochastic block models (SBM). We show that our SBM-based approach constitutes a more principled and versatile framework for topic modeling solving the intrinsic limitations of Dirichlet-based models through a more general choice of nonparametric priors. It automatically detects the number of topics and hierarchically clusters both the words and documents. In practice, we demonstrate through the analysis of artificial and real corpora that our approach outperforms LDA in terms of statistical model selection.