The graph represents a network of 2,418 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 Wednesday, 27 November 2019 at 20:47 UTC. The requested start date was Monday, 25 November 2019 at 01:01 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 5-day, 4-hour, 57-minute period from Tuesday, 19 November 2019 at 20:03 UTC to Monday, 25 November 2019 at 01:01 UTC.

Our cities are growing at an uncontrollable rate. The UN estimates that there are now 33 megacities with a population of over 10 million, (five in India and six--or more--in China), and the largest city in the world, Tokyo, has close to 37.5 million people. As cities sprawl into green space and their inhabitants endure increasingly cramped and polluted conditions, accurate planning about how urban spaces function is more important than ever. With the climate crisis looming, data and new technology could be our best option to create more livable and sustainable cities. Part one of this series will focus on visualizing how cities are growing, how to plan them more accurately and sustainably, and explore how smart technologies can make cities more efficient now and in the future.

Innovative tech, AI and machine learning is changing the game. Insurance AI and Innovative Tech USA 2020 (May 12– 13, Chicago) is the only forum uniting senior insurance data and analytics, technology and business unit executives to explore strategies to achieve both efficient and seamless operations and turbocharged growth.

China's history of censoring the internet and using propaganda to give its citizens a false sense of reality is well documented. And now, Chinese regulators have announced new rules that will allow them to further dictate what citizens perceive as reality. From January 1, 2020, publishing and distributing what the regulators deem to be "fake news" created with artificial intelligence (AI), deep learning, or virtual reality (VR) will be banned. The Cyberspace Administration of China (CAC) added that content produced with AI, deep learning, or VR will need to be clearly labeled when these new rules come into effect. Failure to label such content under the new rules could be a criminal offense.

The Cyberspace Administration of China (CAC) announced on Friday that it is making it illegal for fake news to be created with deepfake video and audio, according to Reuters. "Deepfakes" are video or audio content that have been manipulated using AI to make it look like someone said or did something they have never done. In its statement, the CAC said "With the adoption of new technologies, such as deepfake, in online video and audio industries, there have been risks in using such content to disrupt social order and violate people's interests, creating political risks and bringing a negative impact to national security and social stability," according to the South China Morning Post reporting on the new regulations. The CAC's regulations, which go into effect on January 1, 2020, require publishers of deepfake content to disclose that a piece of content is, indeed, a deepfake. It also requires content providers to detect deepfake content themselves, according to the South China Morning Post.

In 2018, OpenAI found that the amount of computational power used to train the largest AI models had doubled every 3.4 months since 2012. The San Francisco-based for-profit AI research lab has now added new data to its analysis. This shows how the post-2012 doubling compares with the historic doubling time since the beginning of the field. From 1959 to 2012, the amount of power used doubled every two years, tracking Moore's Law. This means the resources used today are doubling at a rate seven times faster than before.

November 30, 2019 -- Fujifilm Medical Systems U.S.A. is showcasing REiLI, the company's global medical imaging and informatics artificial …

China's state-run Global Times described the plan as a fusion of two powerful recent technologies that could insulate help to insulate Huawei from U.S. sanctions by making the Chinese company less dependent on chips from Western providers:

While 67 percent of aerospace and defense companies have adopted AI in at least one business unit--or are piloting the technology--only 19 percent scale many of their digital pilots and deliver superior returns on those investments.2 These challenges are made more critical by the trend towards transformation driven by new, as-a-service business models. Over 85 percent of aerospace and defense executives say they plan to invest in these innovative business models3. AI will play a critical role in helping to successfully design and deliver these new business models. For example, Airbus Aerial is using AI to provide customized analysis to customers across a range of industries including insurance, public utilities and infrastructure.

Over the last couple of years, powerful deep learning methods have emerged to build industrial scale natural language understanding applications. The first wave of deep learning models employed pre-trained word embeddings (word2vec or GloVe) to initialize the first layer of a neural network followed by a task specific model trained using labelled data. The next wave of deep learning architectures (ELMo, ULMFiT, BERT) showed how to learn contextual word embeddings from massive amounts of unlabelled text data and then transfer this information to a wide variety of downstream tasks such as sentiment analysis, question answering etc. with limited amounts of labelled data. This approach is quite relevant for industrial settings where obtaining large amounts of labelled data is expensive. In this hands on tutorial, we will cover the important concepts behind recent developments such as word embeddings, sequence to sequence models, attention mechanism, contextual word embeddings, transfer learning and probing embeddings.