Over the last few years, India has taken significant steps towards adoption of emerging technologies like artificial intelligence (AI) and machine learning(ML), with technology solutions providers, tech leaders, startups and government agencies playing a significant role in shaping the evolution of the technology in the country. According to a recent study, which observed the country-wide AI readiness in the Asia Pacific, India was ranked third in readiness, with its overall readiness score being 50.2 out of100, while Singapore was ranked the first with 63 points and Hong Kong was at the second with 56.5 points. As straightforward as it might sound, AI readiness simply does not refer a country's preparedness in embracing AI, rather, a number of key factors like the ability of its consumers, businesses and government to adopt, deploy and support AI technologies are taken into consideration to better understand the readiness capability of a country in adopting AI. In other words, AI readiness is not a linear process instead, multiple factors shape the outcome. "AI adoption is fragmented and uneven across the region. In some cases, governments' efforts and commitment have yet to be reflected in businesses' or consumers' adoption and usage of AI. In others, business and consumers are taking the lead, showing governments the way forward in terms of change and innovation," Eric Loeb EVP, Global Government Affairs points out in the study.

Hailo, an AI startup based in Israel, has released its initial chip that the company claims is "the world's top performing deep learning processor," with the Hailo-8 chip claimed to deliver 26 tera-operations pers second (TOPS), while consuming only a few watts of power. If true, that would certainly put it near or at the top of its class in performance for edge applications in areas like self-driving cars, drones, smart appliances, and virtual/augmented reality devices. The challenge in these edgey environments has always been to get AI processors with the requisite performance for these applications but consuming only the small amounts of power available in these settings. In fact, Hailo is positioning its new offering as chip that "enables edge devices to run sophisticated deep learning applications that could previously run only on the cloud." However, doesn't mean Hailo-8 is as powerful as a top-of-the-line inference GPU for the datacenter.

The reintroduction of the Artificial Intelligence in Government Act this month is a much needed response to concerns that the United States is lagging behind both foreign governments and American industry in reaping the promise and perils of artificial intelligence. Sponsored by a bipartisan group of senators, the bill promotes the adoption of artificial intelligence in the federal government, while addressing the potential negative consequences. A companion bill was introduced in the House. Central to the bill is the creation of an Artificial Intelligence Center of Excellence within the General Services Administration, which will provide the technical expertise, research, and advice to federal agencies on the acquisition and use of artificial intelligence technology, including all of the accompanying "economic, policy, legal, and ethical challenges and implications." A key part of its mission is to direct and assist the agencies in developing and maintaining governance plans for their use of artificial intelligence.

We are excited to kick off our first Embedded ML workshop - a combination of presentation and ideathon. Firstly, we will talk about the promise of light-weight deep neural networks for energy-efficient and low-cost IoT applications. We discuss some examples of accelerated and low-memory version of deep learning models for real-time use, predictive maintenance, time-series analysis, and demand forecast. We focus on AI methods for turning IoT data into insights and actions. After the introductory talk about Edge AI we will build teams of 3-5 people and have 1.5 hours to come up with project ideas for embedded ML scenarios that include use case, feasibility assessment, workflow, allocation of resources (human, time, computation, ...).

The government has unveiled the membership of its first AI Council as it attempts to position the UK as a leader in the burgeoning sector. The panel includes representatives from Google, Microsoft and Amazon, as well as data protection groups, academia and the public sector. "[Our AI Council will leverage] the knowledge of experts from a range of sectors to provide leadership on the best use and adoption of artificial intelligence across the economy," the digital secretary Jeremy Wright (pictured) will say in a speech at Viva Tech in Paris on Thursday (16 May). "Under the leadership of Tabitha Goldstaub the Council will represent the UK AI Sector on the international stage and help us put in place the right skills and practices to make the most of data-driven technologies." It is expected that the council will eventually draw together a wider group of representatives to address issues facing the UK's AI sector, such as data and ethics, adoption, skills and diversity.

Even in traditional industries, aspirations are high. If there was a single overarching theme in our conversations with executives, it was the desire to succeed in digital and AI by developing new offerings and changing their companies' business models. For example, RBL Bank has launched India's first profitable open banking initiative. By opening its APIs to the public, RBL is allowing other companies to develop innovative services for the bank's customers. We see this trend in other industries, too.

ABBYY, a global leader in Content IQ technologies and solutions, today announced it has signed an agreement to acquire Philadelphia, Pennsylvania-based TimelinePI. TimelinePI provides a comprehensive process intelligence platform designed to empower users to understand, monitor and optimize any business process. The global process analytics market size is expected to grow to USD 1,421.7 million by 2023 according to Research and Markets. The acquisition of TimelinePI is a strategic investment by ABBYY into the emerging process intelligence market which is critical to truly understanding the impact and effectiveness of business processes and opportunities for productivity gains from digital transformation investments. TimelinePI's vision of combining the most versatile process mining and operational monitoring with cutting-edge, process-centric AI and machine learning will serve as a critical cornerstone to ABBYY's Digital IQ strategy.

This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. It depends who you ask. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. AI might be a hot topic but you'll still need to justify those projects.

We have come to the fifth month of the year, and technology especially the disruptive one that includes Artificial Intelligence (AI) is gaining strong-hold more than ever. Understanding its disruptive factors is important as it enables more accurate forecasting and better planning for civil society, policymakers and businesses. Identifying the main levers that drive the growth of AI applications can help to expedite the many positive use cases in the pipeline; like optimised renewable energy distribution at scale and Machine Learning disease diagnosis systems in healthcare. So how are the disruptive technologies redefining businesses sphere? Over the years, it is been seen that AI adaptability is increasing.

Allied Market Research recently published a report, titled, "Automotive Artificial Intelligence Market by Component (Hardware, Software, and Service), Technology (Machine Learning & Deep Learning, Computer Vision, and Natural Language Processing), and Application (Semi-Autonomous and Autonomous): Global Opportunity Analysis and Industry Forecast, 2017 – 2025." The report offers a detailed analysis of top investment pockets, top winning strategies, drivers & opportunities, market size & estimations, competitive landscape, key segments, and changing market trends. According to the report, the automotive AI market was pegged at $445.81 million in 2017 and is anticipated to hit $8.89 billion by 2025, registering a CAGR of 45.0% from 2018 to 2025. Rise in demand for enhanced user experience as well as convenience features and growing demand for autonomous vehicle have fueled the growth of the global automotive AI market. On the other hand, rise in various security & privacy concerns hamper the growth to certain extent.