Ratul has more than 11 years of experience in technology and advanced analytics, machine learning and AI systems, primarily focused on consumer and SME lending. Ratul worked in leading credit bureau Transunion Cibil in India, where he worked more on automation and machine learning using the power of Big data. Earlier Ratul also worked in Global data science leader SAS in its Research and Development office in Pune, India. Ratul holds a B.Tech from National Institute of Technology, Calicut (India). Analytics India Magazine: How important is Data science & AI within Banking Systems in Malaysia?

The High-Level Expert Group on Artificial Intelligence (AI HLEG) will have as a general objective to support the implementation of the European strategy on Artificial Intelligence. This will include the elaboration of recommendations on future-related policy development and on ethical, legal and societal issues related to AI, including socio-economic challenges. Moreover, the AI HLEG will serve as the steering group for the European AI Alliance's work, interact with other initiatives, help stimulate a multi-stakeholder dialogue, gather participants' views and reflect them in its analysis and reports. Advise the Commission on next steps addressing AI-related mid to long-term challenges and opportunities through recommendations which will feed into the policy development process, the legislative evaluation process and the development of a next-generation digital strategy. In May 2019, the AI HLEG will also put forward policy & investment recommendation on how to strengthen Europe's competitiveness in AI, including guidance for a strategic research agenda on AI and on the establishment of a network of AI excellence centres. On the 18th December 2018 the AI HLEG proposed the first draft AI ethics guidelines to the Commission, covering issues such as fairness, safety, transparency, the future of work, democracy and more broadly the impact on the application of the Charter of Fundamental Rights, including privacy and personal data protection, dignity, consumer protection and non-discrimination.

AI and machine learning unmask previously hidden workforce data to make people-centric decisions. Artificial intelligence (AI) and machine learning will finally be woven into workforce management practices, revealing a treasure trove of data organisations have been collecting – but not using – for decades. With regular and digestible access to workforce data trends – like scheduling accuracy, absenteeism, overtime usage, and burnout – predictive analytics will shine, helping organisations head-off potential issues before they arise. Intelligent automation will also free up managers from admin-heavy tasks – like managing schedules, approving time-off requests, and shift changes – while encouraging data-driven decision-making to provide clarity between what is equal versus what is fair. Though, to harness analytical insights to make accurate, actionable decisions for specific employee and business goals, organisations must avoid a "one-size-fits-all" model.

Artificial Intelligence is nothing but the creation of intelligent machines that work and react like humans. Aim of the artificial intelligence is to create intelligent machines. The core problem of artificial intelligence include programming computer for certain traits like as knowledge, reasoning, perception,learning,planning, ability to manipulate. Knowledge engineering is the core part of the Artificial Intelligent research. Machines can act and react like human only when they have huge information about world.

Fortnite Battle Royale has been a runaway success, so much so that it has brought in more than $1bn (£780m), and has broken into mainstream culture in a way few video games do. The rapper 2 Milly, the actor Alfonso Ribeiro and the family of Russell Horning, otherwise known as the Backpack Kid, are suing the company for allegedly copying what they say are their dance creations, and not paying them to do so. Jordan Erica Webber talks to Dr Barbara Lauriat of the Dickson Poon School of Law at King's College London about why we should look to the Elizabethan era to learn more about intellectual property law. She also chats to the Guardian's former Games editor Keith Stuart about what is happening with the lawsuits. The lawyer Alex Tutty then explains why it is unlikely to be an open-and-shut case.

Microsoft's Bing search engine shows results for sickening child pornography images, research has found. The disturbing revelation discovered that it was easy to find illegal photos of under-age boys and girls on the site. Image searches for'porn kids,' 'porn CP' (a known abbreviation for'child pornography') and'nude family kids' all produced the exploitative content. People looking for the horrific content only needed to turn off SafeSearch filter to find the imagery. An investigation commissioned by TechCrunch found that Bing also suggested other disturbing phrases to help paedophiles target children.

Judge Haywood Gilliam previously blocked an interim version of those rules -- a decision that was upheld in December by an appeals court. But the case is before him again after the administration finalized the measures in November, prompting a renewed legal challenge by California and other states.

Most of us are wondering about "what's next for AI in 2019 leading up to 2020?" Some of us might be wondering about "Singularity". Others are still thinking, "AI is all hype and no action" (Although this subset is on the decline). All valid questions to ponder upon. Artificial Intelligence has made substantial progress in 2018.

With many calling for federal regulation of Artificial Intelligence (AI), AAF's Director of Technology and Innovation Policy Will Rinehart explains in a new Primer how policymakers should think about AI and the government's approach to it. He also lays out a policy agenda for Congress and the executive branch.

Variable importance is central to scientific studies, including the social sciences and causal inference, healthcare, and in other domains. However, current notions of variable importance are often tied to a specific predictive model. This is problematic: what if there were multiple well-performing predictive models, and a specific variable is important to some of them and not to others? In that case, we may not be able to tell from a single well-performing model whether a variable is always important in predicting the outcome. Rather than depending on variable importance for a single predictive model, we would like to explore variable importance for all approximately-equally-accurate predictive models. This work introduces the concept of a variable importance cloud, which maps every variable to its importance for every good predictive model. We show properties of the variable importance cloud and draw connections other areas of statistics. We introduce variable importance diagrams as a projection of the variable importance cloud into two dimensions for visualization purposes. Experiments with criminal justice and marketing data illustrate how variables can change dramatically in importance for approximately-equally-accurate predictive models.