Artificial Intelligence, or AI, is all around us and we might not even realize it! As a technology that imitates human decision making, it is present in a lot of what we use every day. AI can be found in Amazon.com Facebook tells you whom you could know and want to be friends with. Cars can automatically park for you.

What I am saying applies specifically to Grid, the "revolutionary" startup company that was the first to announce that it has created a website builder that uses artificial intelligence to design the perfect web destination. Reviews, however, have been rather mixed, not quite perfect as they promised. The involvement of the user is minimal, which, on the plus side, goes to show that whatever the end website looks like is entirely credited to the AI itself and not the person pressing the buttons. That, however, also means that the user has less than enough freedom to customize their website's layout as per their wishes, even if they are a veteran in the subject. The AI limits the website's customizability to a few choices of layout and color only, asking the user to accept the fact that the algorithm knows best.

MetaMind joins PredicitonIO, an open source machine learning server and developer community, which was acquired by Salesforce in February, and the machine learning powered analytics startup BeyondCore in August. This follows the acquisition of MinHash - the company behind the AI virtual assistant AILA - in December 2015, and smart calendar app Tempo.ai in June last year, an acquisition which almost certainly led to the recent Salesforce Inbox Calendar product. Speaking to Computerworld UK earlier this year, CTO Adam Evans said: "AI is technology that Salesforce is incredibly interested in. At Salesforce we have moved from systems of record to systems of engagement and now we are moving towards systems of intelligence."

Vinod Iyengar is the director of product marketing at open source machine learning company H2O.ai. Forget Hillary Clinton and Donald Trump -- we need a trust-buster like Teddy Roosevelt, who in 1902 attacked the Northern Securities Company, a railroad holding company led by J.P. Morgan and James J. Hill. Amid the flurry of recent tech M&A deals, you would be forgiven for missing a relatively quiet acquisition: Apple bought Seattle-based artificial intelligence startup Turi for around 200 million. On its own, the Turi deal wouldn't raise eyebrows were it not for the record amount of M&A in artificial intelligence, with the giants Alphabet, Amazon, Apple, Facebook and Microsoft leading transactions. Consolidation in any market is expected, but AI is consolidating at such a rapid clip, at such an early stage and by so few companies that there are strong reasons to be concerned.

In a previous post I advised data scientists in training to build stuff. This post gets more specific. Here's what I mean when I say I'm doing data science. The raw stuff of data science is a collection of numbers and names. Measurements, prices, dates, times, products, titles, actions--everything is fair game. You can use images, text, audio, video and other complex data too, as long as you have a way to reduce it to numbers and names. The mechanics of getting data can be quite complex.

The Institution of Engineering and Technology (IET), a publisher of widely respected publications and information services for engineers and technicians, has chosen Ontotext's GraphDB to help it improve text indexing and discoverability in its Inspec database. With Ontotext's powerful graph database tool, the IET is continuing its reputation for innovative excellence by applying semantic technology to its text indexing. This technology promises to improve research access and deliver a superior experience to contributors and users. The Inspec database contains more than 16 million highly curated and specialised journal articles, conferences and videos relating to engineering and physics research, developed over the last 40 years. As an authority in the engineering world, the IET has accumulated deep expertise in cataloguing the wealth of content it maintains.

Every once in a while, a product comes along that changes everyone's expectations of what's possible in user interfaces. Alexa belongs in that elite group of game changers. Siri didn't make it over the hump, despite the buzz it created. Neither did Google Now or Cortana, despite their amazing capabilities and their progress in adoption. But Alexa has done so many things right that everyone else has missed that it is, to my mind, the first winning product of the conversational era.

Back in 2001, when I started working in the enterprise marketing software business, customer relationship management or CRM was seen as the cure all from a sales and marketing perspective. "If only we could more quickly send direct mailers offering a buy one, get one video rental, we could corner the market" one executive told me. CRM deployments at that time were costly and resource intensive. My how times have changed. But one thing hasn't changed – there remains three critical components to consider when standing up a solid customer intelligence software solution – data, insight and action. Data meaning a centralized data repository – containing first and third-party data.

Apple wants you to know that Siri is no dunderhead. The normally secretive tech giant lifted the lid on its artificial intelligence efforts by inviting the writer Steven Levy to its headquarters to hear it crow about all the products and services that are benefiting from various machine-learning techniques, especially deep learning. The resulting article, published on Levy's site, Backchannel, paints a picture of a company keen to boast about its AI chops. It's certainly interesting to hear about how deep learning is being used to improve Siri's hearing, voice, and natural language; and how machine learning is helping to make other Apple products smarter. But it's also hard not to notice that the company is starting to lag behind some of its competitors.

The decision by consulting major Capgemini to replace nearly 40% of its work done by its resource management group with IBM's cognitive computing system, Watson, is a clear indication that it is not just repetitive or mechanical jobs that are at risk. Artificial intelligence (AI) is capable of taking on those tasks that require analytical skills. The tasks from education and skill development just got tougher. By 2025, 70% of India's population is projected to be of working age. A chunk of India's present knowledge economy would have been chomped down by AI.