That's an adage that is hopefully familiar to all privacy professionals. It's certainly something we talk a lot about in the office. I think we're learning that, when taken on as a corporate philosophy, applying ethical considerations to your products and services can go a long way in procuring user trust. An article from my colleague, Angelique Carson, for example, on whether the profession needs an ethical code has generated a lot of interesting discussion surrounding a privacy pro's obligation to not simply advise a company regarding legality, but to advocate on behalf of the consumer. In particular, advanced technology is putting pressure on ethical considerations.

If you're as plugged in a we are, then you probably have something like 150 online accounts. That means you probably have 150 variations of the same half-dozen login names and passwords. And chances are you've gotten a message from a friend in the last few months telling you that it looks like you got hacked again, after he received email spam to enlarge an embarrassingly small body part. Well, we think the days of the alphanumeric password are numbered. The age of biometric authentication is dawning.

Fortune magazine last week released a list of "50 Companies Leading the AI Revolution," and the prestigious list includes three hot Israeli companies in the artificial intelligence sector: Logz.io, Fortune's infographic includes only six countries and features an equal number of notable AI companies from Israel (population 8.5 million) as China (population 1.38 billion) and the United Kingdom, and more than France and Taiwan. Only the United States has more companies on the graph. Fortune relied on research firm CB Insights' AI 100 list of the most promising artificial intelligence startups globally, based on factors like financing history, investor quality, business category, and momentum. The CB Insights list also includes Israeli companies Prospera Technologies (ag-tech at work in Spain, Mexico, and New York) and Chorus.ai

According to analyst Forrester, artificial intelligence (AI), big data and analytics will increase businesses' access to data, broaden the types of data that can be analysed, and raise the level of sophistication of the resulting insight. For 2017, Forrester expects investment in AI to triple. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.

In the hallowed hallways of Morgan Stanley's office in Canary Wharf, men in crisp tailored suits pass each other, briskly chatting away on their smartphones, while attractive young women in black pencil skirts and long legs scamper through the halls with take-away salads from Chop'd, dressing on the side. Walking these very halls has been the aspiration of many, the full-time MBA candidate who was looking for a career change and "wanted to get into investment banking". Just what is investment banking, you might ask? Well, the money-making part of the operation is called "the front office". Let's just say that if you meet a 9 at All Bar One in Canary Wharf and she asks you if you're in a front office role, just say yes.

With many companies still stuck to take advantage of data, analytics has to be number one question because this is a key stage to implement successfully AI. For employees in the analytics business, there is a natural progression from analytics to artificial intelligence. There is a sequence of evolution in analytics, starting from descriptive to prescriptive. Many enterprises use the descriptive analytics, applying BI techniques: combine all your data to get a quick review on what's going on in the company. Diagnostic analytics includes data discovery, data mining, drill-down, and correlations.

CustomerMatrix is a New York company, but Mounier is calling from Paris today – that's where the company first started. Paris is also the location of 40 R&D people working on maintaining the company's artificial intelligence engine – you could also call it machine learning or cognitive computing. In any case, what the CustomerMatrix platform does is attempt to perform tasks in a manner similar to the human brain: take in lots of information, identify patterns, draw out meaning, and make decisions. IBM's Watson is the headline example for how this can work, but Mounier says the CustomerMatrix approach is more focused: "We're already connected to all the customer data sources on the internet. For example, we go on LinkedIn, we go on FactSet, we have relationship with Factiva, and in the US we even have a relationship with eBay."

Over the past few years technology has been finding a way to produce more and more innovative ways to diagnose disease, but now it has a new party trick – disease detection by voice analysis – and it's just as it sounds, if you excuse the pun. You talk, it analyses your voice and screens for illnesses. We're all used to being able to tell when someone's ill by just listening to their voice – on the phone for example. If someone has a cold then they sound nasal, and we know that and we can ask them if they have a cold. If they're slurring then again we can hear it and we can ask them what's wrong.

I'm a software engineer by training and I've had little interaction with AI. I had always wanted to delve deeper into machine learning, but never really found my "in". That's why when Google open sourced TensorFlow in November 2015, I got super excited and knew it was time to jump in and start the learning journey. Not to sound dramatic, but to me, it actually felt kind of like Prometheus handing down fire to mankind from the Mount Olympus of machine learning. In the back of my head was the idea that the entire field of Big Data and technologies like Hadoop were vastly accelerated when Google researchers released their Map Reduce paper.

Everyone today talks about AI, big data and machine learning, yet most do not delve into the fundamental properties of how they will operate and how they might be an actual threat...