Colombo is talking about artificial intelligence. Millennium IT Founder and other companies Tony Weeresinghe delivered an oration on AI to the alumni of the University of Colombo. Vallibel One Group Founder Chairman and power behind many others Dhammika Perera delivered a two-hour monologue on the subject at an event organised by the Computer Society of Sri Lanka a few weeks back. To become a knowledge-based economy we have to understand and ride the waves of technology that keep rolling into this island. Beyond that, we must contribute to the world's knowledge.

IBM's Chair, CEO and President Ginni Rometty has a powerful message for workers and employers in all strata of society: The Fourth Industrial Revolution is underway and it is shaping up to be one of the most significant challenges and opportunities of our lifetime. We are already seeing jobs, policies, industries and entire economies shifting as our digital and physical worlds merge. According to the World Economic Forum, the value of digital transformations in the Fourth Industrial Revolution is estimated at $100 trillion in the next 10 years alone, across all sectors, industries and geographies. "As a result, we face an imminent and profound transformation of the workforce over the next five to 10 years as analytics and artificial intelligence change job roles at companies in all industries," Rometty said while giving a keynote address at the CNBC's At Work Talent & HR: Building the Workforce of the Future Conference in New York on Tuesday, April 2. In February, the executive was appointed to Trump's American Workforce Policy Advisory Board along with 24 other leaders. While only a minority of jobs will disappear, the majority of roles that remain will require people to work with the aid of analytics and some form of AI and this will require skills training on a large scale, Rometty said.

If you've seen the Terminator then you know just how scary AI run amok can be. Which is why it's no surprise the European Commission's (EC) just unveiled 7 laws tech companies will have to follow when making artificial intelligences. We thought so, especially when you compare them to the three laws of robotics seen in Isaac Asimov's seminal Sci-Fi works, which cover the important stuff, like robots not taking over the world, or killing people. Despite the lack of a firm "don't kill humans" law the EC seems pretty chuffed with its guidelines. Vice-President for the Digital Single Market Andrus Ansip said the laws were created in partnership with industry experts and include robust "ethical" elements.

Yoshua Bengio: Yoshua BengioOCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadiancognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.

The European Commission will launch a pilot project this summer designed to test ethical guidelines it has developed for the use of artificial intelligence. Companies, public agencies, and other organizations can now join the European AI Alliance which will officially notify members when the pilot starts. "The ethical dimension of AI is not a luxury feature or an add-on," said Vice-President for the Digital Single Market Andrus Ansip in a statement. "It is only with trust that our society can fully benefit from technologies. Ethical AI is a win-win proposition that can become a competitive advantage for Europe: being a leader of human-centric AI that people can trust."

As Lyft and Uber are on the verge of going public Elon Musk has announced that Tesla too plans to join the lift-sharing sector-albeit in a slightly different way. The news was announced via a response to a twitter post from @LivingTesla who complained about the camera on the rearview mirror of the car. The Tesla fan stated that until they know its purpose they would cover the camera. Musk responded saying the camera was installed there to monitor the interior of the car during a rideshare type experience once the car becomes apart of the "Tesla shared autonomy fleet." It's there for when we start competing with Uber/Lyft & people allow their car to earn money for them as part of the Tesla shared autonomy fleet.

One magical aspect of software is that it just keeps working. If you code a calculator app, it will still correctly add and multiply numbers a month, a year, or 10 years later. The fact that the marginal cost of software approaches zero has been a bedrock of the software industry's business model since the 1980s. This is no longer the case when you are deploying machine learning (ML) models. Making this faulty assumption is the most common mistake of companies taking their first artificial intelligence (AI) products to market.

Businesses across verticals are moving from digitisation to cognification of everything. Having said that, banks and financial institutions have recognised the potentials of Artificial Intelligence (AI) to redefine their processes, products and services. With customer experience becoming vital to ensure good business, banks have been adopting AI solutions to further enhance their services what with virtual assistants and chatbots handling different customer queries. The banking industry is using AI to reimagine products, processes, strategies and the overall customer experience. Cutting edge AI research and development is transforming the sector through an automated, integrated, collaborated approach to cyber defence and helping facilitate better information sharing between security components within and across organizations. In the current scenario, four new threat samples are submitted to our systems every second, which is around 250 samples every minute or 15,000 samples every hour, equivalent to approximately 3,60,000 NEW samples daily!

In this paper, we propose and investigate a variety of distributed deep learning strategies for automatic speech recognition (ASR) and evaluate them with a state-of-the-art Long short-term memory (LSTM) acoustic model on the 2000-hour Switchboard (SWB2000), which is one of the most widely used datasets for ASR performance benchmark. We first investigate what are the proper hyper-parameters (e.g., learning rate) to enable the training with sufficiently large batch size without impairing the model accuracy. We then implement various distributed strategies, including Synchronous (SYNC), Asynchronous Decentralized Parallel SGD (ADPSGD) and the hybrid of the two HYBRID, to study their runtime/accuracy trade-off. We show that we can train the LSTM model using ADPSGD in 14 hours with 16 NVIDIA P100 GPUs to reach a 7.6% WER on the Hub5- 2000 Switchboard (SWB) test set and a 13.1% WER on the CallHome (CH) test set. Furthermore, we can train the model using HYBRID in 11.5 hours with 32 NVIDIA V100 GPUs without loss in accuracy.

Traditional motion planning approaches for multi-legged locomotion divide the problem into several stages, such as contact search and trajectory generation. However, reasoning about contacts and motions simultaneously is crucial for the generation of complex whole-body behaviors. Currently, coupling theses problems has required either the assumption of a fixed gait sequence and flat terrain condition, or non-convex optimization with intractable computation time. In this paper, we propose a mixed-integer convex formulation to plan simultaneously contact locations, gait transitions and motion, in a computationally efficient fashion. In contrast to previous works, our approach is not limited to flat terrain nor to a pre-specified gait sequence. Instead, we incorporate the friction cone stability margin, approximate the robot's torque limits, and plan the gait using mixed-integer convex constraints. We experimentally validated our approach on the HyQ robot by traversing different challenging terrains, where non-convexity and flat terrain assumptions might lead to sub-optimal or unstable plans. Our method increases the motion generality while keeping a low computation time.