Regardless of the end goal of your data science solutions, an end-user will always prefer solutions that are interpretable and understandable. Moreover, as a data scientist you will always benefit from the interpretability of your model to validate and improve your work. In this blog post I attempt to explain the importance of interpretability in machine learning and discuss some simple actions and frameworks that you can experiment with yourself. In traditional statistics, we construct and verify hypotheses by investigating the data at large. We build models to construct rules that we can incorporate into our mental models of processes.

Artificial intelligence (AI) could be deployed by dictators, criminals and terrorists to manipulate elections and use drones in terrorist attacks, more than two dozen experts said Wednesday as they sounded the alarm over misuse of the technology. In a 100-page analysis, they outlined a rapid growth in cyber crime and the use of "bots" to interfere with news gathering and penetrate social media among a host of plausible scenarios in the next five to 10 years. "Our report focuses on ways in which people could do deliberate harm with AI," said Sean O hEigeartaigh, Executive Director of the Cambridge Center for the Study of Existential Risk. "AI may pose new threats, or change the nature of existing threats, across physical, political and cyber security," he told AFP. The common practice, for example, of "phishing" -- sending emails seeded with malware or designed to finagle valuable personal data -- could become far more dangerous, the report detailed.

There has always been a huge amount of excitement around topics like Artificial Intelligence(AI). When someone mentions AI, you either think about AIs like Siri and how our lives have changed over the years because of it, or your thoughts might take a dark turn and wonder when Skynet is going to take over the world! Rest assured, that is not going to happen anytime soon! We hear about AI every day. We hear about AI in our science fiction movies and we also talk to an AI on our phones.

Have you ever wondered why doctors examine your eyes even when the ailment is seemingly somewhere else? One's eyes, or rather the retina, doctors say, can reveal a lot about your general well being. It turns out what doctors can, Google can do better. On Tuesday, Google published an official blog saying its deep learning algorithm can accurately predict a person's cardiovascular health by evaluating retinal images. In other words, Google's AI can tell if your heart is in a good state. The discovery was put forward by scientists Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, and Greg S. Corrado in a research paper titled'Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning', published in Nature Biomedical Engineering.

I recently caught up with Kobi Stok, Vice President, Product at WalkMe, to give an insiders view for how his company is using AI to predict how/when you're using a mobile app, and how companies can use this info to push customized campaigns to prevent someone from leaving the app. Kobi is VP, Product at WalkMe, a leading digital adoption platform that simplifies user experiences and drives action using insight, engagement and guidance capabilities. Prior to joining WalkMe he was the Cofounder and CTO of Abbi.io, an award-winning technology that learns and understand user usage behavior. A serial entrepreneur, Kobi has over a decade's experience in building mobile and web-based software from design to customer deployment. Can you provide a high-level overview of how AI is being used?

The development of a product into full-fledged service/s on cloud has seen the rise of new services such as Platform as a service (PaaS), Infrastructure as a service (IaaS) and Software as a service (SaaS). Their growth as a market has led to a battle in the cloud space market. Joining these cloud-based services and slowly opening up another competition is Machine Learning as a service (MLaaS). The growing trend of shifting data storage to cloud, maintaining it and deriving the best insights from it has found an ally in MLaaS which provides these solutions at a reduced cost. Machine learning as a service (MLaaS) is an array of services that provide machine learning tools as part of cloud computing services.

I'm definitely not in some reputed position to be commenting or throwing irrational opinions around or take sides on such great thought processes that had been born decades before I was born, but consider these as thoughts of someone who has been closely following the works of pioneers of the field. I am just a believer of the idea that if AGI can be created one day in the future then deep learning is definitely going to have a vital role to play in its proper functioning. Companies like OpenAI inspire me and many others to believe that putting in hours into research and thinking about applications of AI will definitely yield amazing results that will revolutionize the way we live our lives. It would be something combining different fields of study like neuroscience, philosophy, mathematics, physics, computer science and many more that would together contribute in a masterpiece that would prove to be the best creation of mankind.

The Adobe Research team has been exploring what the future may hold for selfie photography powered by Adobe Sensei. Great portrait photography requires the right perspective, equipment, and editing expertise. But what happens when we tap into the power of artificial intelligence and deep learning to transform bad portrait shots into good ones – all on a smartphone? By combining perspective effect editing, automatic, software-only photo masking, and photo style transfer technology, we're able to transform a typical selfie into a flattering portrait with a pleasing depth-of-field effect that can also replicate the style of another portrait photo.

Tesla CEO Elon Musk is stepping down from OpenAI's board amid concerns of a possible conflict of interest. Elon Musk will step down as chairman of OpenAI, the nonprofit artificial intelligence research company he co-founded two years ago, amid concerns of a possible conflict of interest. Musk, the CEO of Tesla and SpaceX, will depart the board but continue to "donate and advise the organization," the OpenAI said in a blog post Tuesday. "As Tesla continues to become more focused on AI, this will eliminate a potential future conflict for Elon," OpenAI said. Research in artificial intelligence, a term used for the ability of machined, computers or systems to exhibit humanlike intelligence, has been dominated lately by large tech companies such as Google and Facebook.

– Big data helps to make strategy for future and understand user behaviors. In 1959, Arther Samuel gave very simple definition of Machine Learning as "a Field of study that gives computer the ability to learn without being explicitly programmed". Now almost after 58 years from then we still have not progressed much beyond this definition if we compare the progress we made in other areas from same time. The idea of FinTech adopting some best practices from the Big Data and AI (Artificial Intelligence, Machine Learning and Deep Learning) is not so new, have you heard of accepting selfie as authentication for your shopping bill payment, Siri on your iPhone etc. A Decentralized Autonomous Organization (DAO) is a process that manifests these characteristics.