Helsinki, Finland - An audience of international peace brokers have gathered inside a room in the historic House of Estates. They have come from South Sudan, the Central African Republic, Ukraine, Colombia and elsewhere to hear a scientist speak. That scientist is Timo Honkela, and his keynote speech on the second day of April's National Dialogues conference is titled Peace from a Different Perspective - a Dialogue of a Million People. But 54-year-old Honkela is working on a machine that he hopes will facilitate world peace. "World peace would be a good goal to work for in my remaining days," he says, smiling over a cup of coffee during a break in the conference.

Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. They are not pie in the sky technologies any longer; they are practical tools that can help companies optimize their service provision, improve the standard of care, generate more revenue, and decrease risk. Nearly all major companies in the healthcare space have already begun to use the technology in practice; here I present some of the important highlights of the implementation, and what they mean for other companies in healthcare. AI, machine learning, and deep learning are already increasing profits in the healthcare industry. For example, according to research firm Frost & Sullivan by 2021, AI systems will generate $6.7 billion in global healthcare industry revenue.

At the SIS Global Forum 2019, Chevron, Microsoft and Schlumberger, announced the industry's first three-party collaboration to accelerate the creation of innovative digital and petro-technical technologies. The three companies will work together to build Azure-native applications in the DELFI cognitive E&P environment initially for Chevron, which will enable companies to process, visualize, interpret and eventually obtain meaningful and vital insights from numerous data sources. The collaboration will be completed in three phases, starting with the deployment of the Petrotechnical Suite in the DELFI environment. This will be followed by the development of cloud-native applications on Azure and the co-innovation of a suite of cognitive computing native compatibilities across the E&P value chain tailored Chevron's objectives. "Combining the expertise of these three global enterprises creates vastly improved and digitally-enabled Petrotechnical workflows. Never before has our industry seen a collaboration of this kind, and of this scale. Working together will accelerate faster innovation with better results, marking the beginning of a new era in our industry that will enable us to elevate performance across our industry's value chain," said Olivier Le Peuch, Chief Executive Officer, Schlumberger.

The AI market is possibly one of the toughest to keep track of, as the pace of change is relentless. Making predictions is even harder for this market, as one tends to follow what is known as Amara's Law, overestimating the potential of a technology in the short term and underestimating it in the longer term. Nevertheless, Tractica has identified 10 key predictions that cover various aspects of the ever-evolving AI market, based on our ongoing research and analysis including extensive primary research and interviews. Key trends to watch in the AI market in 2019 will include the commercialization of promising technologies, the growth of new application markets, changes in hardware architectures and infrastructure to support AI deployments, and continuing evolution of business models and public policy issues. Tractica's 10 key predictions for AI in 2019 include the following: This Tractica white paper identifies 10 key predictions for the AI market in 2019 and provides supporting details and examples behind each prediction. These trends draw from ongoing research and analysis that form part of Tractica's Artificial Intelligence advisory service.

The pace of technological change is rendering many job activities -- and the skills they require -- obsolete. Research by McKinsey suggests that globally more than 50% of the workforce is at risk of losing their jobs to automation, and a survey by the World Economic Forum suggests that 42% of the core job skills required today will change substantially by 2022. In this landscape of constant disruption, individuals, companies, and governments are fighting to ensure they have the skills to remain competitive. To shed light on the global skills landscape, Coursera recently released the first edition of our Global Skills Index (GSI) report. As the world's largest platform for higher education, Coursera brings together 40 million learners around the world with over 3,000 courses from leading universities and companies.

Machine Learning (ML) provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. The rising technology in Machine Learning in Medicine market is also depicted in this research report. Factors that are boosting the growth of the market, and giving a positive push to thrive in the global market is explained in detail. The report delivers a comprehensive overview of the crucial elements of the market and elements such as drivers, restraints, current trends of the past and present times, supervisory scenario, and technological growth. A thorough analysis of these elements has been accepted for defining the future growth prospects of the global Machine Learning in Medicine market.

National Workshops to Explore Long-range Strategies 7. Collect suggestions from the national planning workshops, distilled in to 93 actions, assess all via five (5) Real-Time Delphi's 8. Final Report for Public Discussion 10.

In today's world it's easy to get swept up in the cool side of Artificial Intelligence. Whether it be a new Amazon Echo, autonomous cars or perhaps the new Boston Dynamics Robot, which now has the amazing ability to open a door (just watch the video on YouTube, you'll see what I mean). However, whilst the cool stuff is put right in front of our eyes, for us to fascinate over it's easy to forget about what goes on behind the scenes of Artificial Intelligence. Now I'm not saying that the tech, gadgets and robots aren't cool, they're very cool. In fact, I'm sure I'll be the first in line to buy an autonomous flying car, when the time comes.

Unemployed Artificial Intelligence (AI) ex perts are as elusive as the abominable snowman of the Himalayas. In fact, in my AI career, which spans more than two decades, I have never met one. However, in the past year, ever since the media woke up to the amazing world of AI, I have been receiving queries like: since AI is a new thing, will I find a job once I graduate? I've never seen an advert for an AI expert, etc. In the past decades, and especially in the past year in my role as a member of the Malta.AI taskforce, I have met thousands of people who might have heard about AI, are probably using it extensively, but have no clue that they're using it or even what it is.

In January-March 2019 The Economist Intelligence Unit, on behalf of Temenos, surveyed 405 global banking executives on the changes they see taking place in their industry to 2020 and 2025, their organisational response, and the longer-term impact on their strategic development. This, the sixth iteration of the retail banking survey, focuses on how these retail banks are incorporating and advancing technology delivery for their current and future customers. The survey is part of a global research programme on retail banking, which includes in-depth interviews with retail banks, fintechs and regulators from North America, Europe, Africa and the Middle East, Asia-Pacific, and Latin America. The survey respondents were geographically diverse: 25% were drawn from Europe, 25% from Asia-Pacific, 18% from North America, 16% from Africa and the Middle East, and 16% from Latin America. Respondents came from a variety of job functions: marketing and sales (18%), IT (15%), and customer service and finance each accounted for about one in ten respondents (9% and 10% respectively).