Scientific determinism as first published by Laplace in 1814 is an important and essential principle in the macro-world around us. We know that if we push something, it will move -- unless our impulse was not sufficient to overcome inertia… and so forth. Laplace postulated that if there were an omniscient daemon who knew the precise positions and impulses of each and every particle in a system, this daemon would be able to deterministically calculate each and every future state of this system. Our beloved spreadsheet calculations resemble this daemon (possibly in more than one connotation). Typing in some basic data to start calculations from, the wonderous spreadsheet software will automagically calulate everything depending on them, eventually deriving the results we wanted to obtain.

If you're looking to add artificial intelligence to a hardware project for doing things like sensing objects in an enviroment or understand voice commands, Movidius' new Fathom USB stick might be just the thing. The company is known for its Myriad 2 deep learning chip that allows DJI drones to avoid obstacles. The Fathom is essentially a portable version of the chip that can be plugged into the USB 3.0 port of Linux-based devices to run fully-trained neural networks while consuming very little power. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May. It's compatible with Caffe and TensorFlow frameworks and is capable of 150 gigaFLOPS (150 billion floating-operations per second). That means developers can use it to do things like enable robots to understand natural human speech and recognize faces, and teach drones to navigate indoors and outdoors by themselves – all without the need to connect to the cloud.

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods. In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological "labels" to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don't use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

Twitter is an extremely popular online social networking and micro-blogging service. Users communicate through "tweets" - these are short 140-character messages or opinions about different topics. This site is a mine of information about users and their interests - their profile, views, attitudes, observations, people they follow on the site, etc. Apart from being used as a channel of communications between family and friends, Twitter is also used for real-time news updates, recommendations and sharing content. Processing all this information will provide marketers and opinion leaders with a wealth of knowledge about consumers and their behavior and enable them to design effective marketing strategies. Join this webinar to learn how to extract, analyse and utilize this data by implementing machine learning algorithm on the available information.

Technologies that most, if not every, technologist is well aware of. Maybe a little more nuanced for some to track? The beauty of being a budding technologist is that the landscape is constantly changing and forever in flux. What was once a herculean task of reading magazines and books with the hopes of finding the necessary information to connect the dots has been trivialized to a simple "online search" or a quick question to your phone. With the general accessibility of the internet, the ability to track and understand the technology landscape has simplified.

With A.I. it's sometimes easy to get ahead of ourselves. Yes, it's true, "computers are going to take over from humans, no question" (Steve Wozniak), and while it is also probably true that soon enough we will all be made into paperclips by a super-intelligent machine that lacks human values, we still have some time. Despite his apocalyptic paperclip predictions (slightly taken out of context) Nick Bostrom would still "assign less than a 50% probability to super-intelligence being developed by 2033". It turns out that A.I. is still hard. Take Microsoft's Tay for example, an A.I. chat bot built to speak'like a teen girl' and be a virtual friend on social media.

A couple of months ago I was at a London insurance market meeting. It was mostly attended by brokers and underwriters and the subject was the London TOM. During a roundtable discussion I mentioned an article I'd just written about big data, artificial intelligence and machine learning. I said as much as 80% of insurance underwriting will be automated before long. Sitting opposite me was a London market broker. "Nothing writes business faster than a Mont Blanc pen!" Now, I'm not trying to make fun at his expense. How could he know any different, he wasn't a subscriber to The Digital Insurer at the time (although he is now!). In the specialist insurance market of London, this mind set may have held the market in good stead since the days of the quill pen.

OpenAI, a 1 billion ( 687 million) artificial intelligence company backed by Elon Musk, has built a "gym" where developers can train their AI systems to get smarter. Using OpenAI's open source toolkit, available for download now, developers can access "environments" where they can test their AI bots. The OpenAI Gym, currently in beta, provides a number of environments, including more than 50 Atari games, such as "Space Invaders," "Pong," "Asteroids" and "Pac-Man". Developers can also test their AIs on board games like Go, which was recently mastered by an agent built by London startup Google DeepMind. "Over time, we plan to greatly expand this collection of environments," wrote OpenAI's Greg Brockman and John Schulman in a blog post.

The last few years have seen numerous studies pointing to a bleak future with technology-induced unemployment on the rise. For example, a pivotal 2013 study by researchers at the University of Oxford found that of 702 unique job types in the United States economy, around 47% were at high risk of computerisation. This was backed up by similar findings in Australia suggesting 44% of occupations – representing more than five million jobs – were at risk over the coming 10 to 15 years. Is the situation really so dire? Are we heading towards mass unemployment as computers and robots do all the work?

From the story of Talos of Crete to Mary Shelley's Frankenstein, the concept of artificially-simulated intelligence has long fascinated humankind. This urge to replicate intelligence through synthetic measures is what is even today driving us on to greater innovations. Our online searches are now taking into account the previous'experiences' to curate more tailored results, our cars are becoming self-driven as a result of AI-based tech and robotic domestic helpers have crossed the menial tasks of cleaning homes off the list of their human masters. In short, each and every aspect of our lives is being increasingly taken over by AI, and these examples are barely scraping the tip of the iceberg. To gain a more detailed insight into how ingrained artificial intelligence, heuristic algorithm and machine learning is becoming to everyday functioning, one only needs to take a look at the global startup ecosystem. While traditional technology majors such as IBM, Google, Microsoft and Amazon do figure in prominently when it comes to AI, there are several start-ups and tech-based ventures which are focusing on the technology as a key differentiator for their services and using it to revolutionise the way that businesses are conducted.