Where is the data stored and who has access to it? How is my personal data shared? Today many mobile apps are connected to trackers that collect personal information and sell it.

Episode Summary: One facet of business that nearly any industry has in common is the need to stay on top of news in their respective market, including competitor strategies or understanding changes in news related to the field. Media monitoring is a domain that machine learning (ML) is well suited for, with it's ability to coax out headlines, contextual information, and financial data from the seemingly endless stream of social, blog, and other information on the web today. Signal is a company that uses ML specifically for these purposes. In this episode, we speak with Signal's Chief Data Scientist and Co-founder Dr. Miguel Martinez, who dives into real business use cases illustrating the use of machine learning for media monitoring across industries. Brief Recognition: Dr. Miguel Martinez is chief data scientist at Signal, where he manages a team of data scientists to transform best algorithms from the fields of machine learning, information retrieval, and natural language processing into large-scale commercial products.

"Insurtech", as it's known, is the application of AI to any number of touchpoints across the medical and health insurance spectrum. Real-time geolocated doctor appointment bookings are only the beginning. Imagine sending your doctor snapshots of a wound over time so he can check how it's healing. Or capturing your own EKG at home while your doctors receive alerts if anything is amiss. Even the very hardware many people already use at home for medical purposes (glucometers, for example) might be tied into the IoT.

Here's why banks and credit unions should believe in artificial intelligence and invest in the power of advanced data analytics. Traditional retail banking providers, weighed down by monolithic legacy systems and ponderous regulations, are in uncomfortable territory. Advancements in fintech have upended the industry, enticing both large financial firms and smaller tech startups to apply disruptive technologies in ways that threaten the status quo. To become more agile and remain relevant, traditional retail banking providers find themselves exploring their technological options with focused intensity. New innovations in data analytics empower financial institutions with systems that are so smart, they learn on the go, automatically refining their algorithms and improving their results over time.

It's the beginning of the end for smartphone apps as we have known and tapped on them, reckons Gartner. The analyst is calling the start of a "post-apps" era, based on changes in consumer interactions that appear driven, in large part, by the rise of dominant messaging platforms designed to consume more and more of mobile users' time and attention. It reckons messaging apps will become more popular than social media apps within the next two years. In a new report, based on a survey of mobile users in the US, China and the UK, Gartner's reading of the app usage tea-leaves shows signs of messaging platforms cannibalizing other apps, with for example, usage of dedicated video apps declining four percentage points between the 2015 and 2016 editions of the survey. Usage of standalone maps apps also shrunk by three percentage points, year-over-year, according to Gartner's data.

The shape-shifting, artificial intelligence (AI)-powered cars seen in futuristic films aren't appearing anytime soon but AI technology certainly exists today in the auto market and will be increasingly visible in the future. Industry observers are saying that AI and machine learning have reached a tipping point and will cause enormous advances through the next few years in many markets. In the automotive sector, these technologies will be transformative. Analysis firm IHS Markit predicted that the installation rate of AI-based systems in new vehicles would rise by 109% in 2025, compared to a modest 8% adoption rate in 2015. In February 2017, Ford paid $1 billion for artificial intelligence start-up Argo AI.

Instead of talking about artificial intelligence (AI) many describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing or focus on several real-life applications of artificial intelligence that often start with words such as "smart", "intelligent", "predictive" and, indeed, "cognitive", depending on the exact application – and vendor. Despite the term issues, artificial intelligence is essential for and in, among others, information management, medicine/healthcare, data analysis, digital transformation, security (cybersecurity and others), various consumer applications, scientific advances, FinTech, predictive systems and so much more. There are many reasons why several vendors doubt using the term artificial intelligence for AI solutions/innovations and often package them in another term (trust us, we've been there). Artificial intelligence (AI) is a term that has somewhat of a negative connotation in general perception but also in the perception of technology leaders and firms. One major issue is that artificial intelligence – which is really a broad concept/reality, covering many technologies and realities – has become like a thing we talk about and also seem to need to have an opinion/feeling about, with thanks to, among others, popular culture. Hollywood loves AI (or better: superintelligence, not the same).

AI is clearly a hot topic for insurers. InsurTech startups are creating compelling new use cases and applications for data, algorithms and AI within the insurance space. It is, indeed, an exciting time for insurance. Yet many traditional insurers may read Rick's article with concern and potential fear. In a recent survey by KPMG International, 91 percent of insurance CEOs admitted being worried about the challenge of integrating automation, AI and cognitive robotics into their existing business and operating models.

Recently there has been an explosion of buzz words related to Artificial Intelligence. My goal with this article is to discuss the differences between Artificial Intelligence, Machine Learning and Cognitive Technology. I invite you to join in, it is very likely I am missing elements that you might be more familiar with. Artificial Intelligence (AI) is the hype at the moment. In most cases, the hype is related to a specific subclass of AI known as narrow AI.

US tech giant Apple is reportedly not working on Siri-powered speaker just like Amazon Echo, an artificial intelligence (AI)-based speaker that can be controlled by voice. "The future goal of the technology is to utilize Siri as an'omnipresent AI assistant across devices' rather than have a central hub," Times reported on last week. How Siri would become omnipresent is not known at this point. Earlier this week, Apple acquired Israel-based start-up RealFace that develops deep learning-based face authentication technology. Set up in 2014 by Adi Eckhouse Barzilai and Aviv Mader, RealFace has developed a facial recognition software that offers users a smart biometric login, aiming to make passwords redundant when accessing mobile devices or PCs, as reported yesterday.