A host of different tasks--such as identifying the song in a database most similar to your favorite song, or the drug most likely to interact with a given molecule--have the same basic problem at their core: finding the point in a dataset that is closest to a given point. This "nearest neighbor" problem shows up all over the place in machine learning, pattern recognition, and data analysis, as well as many other fields. Yet the nearest neighbor problem is not really a single problem. Instead, it has as many different manifestations as there are different notions of what it means for data points to be similar. In recent decades, computer scientists have devised efficient nearest neighbor algorithms for a handful of different definitions of similarity: the ordinary Euclidean distance between points, and a few other distance measures.

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First, before I start, I want to say something about what that is, or what I understand from this. So, here is one interpretation. It is about using data, obviously. So, it has relationships to analytics and data science, and it is, obviously, part of AI in some way. This is my little taxonomy, how I see things linking together. You have computer science, and that has subfields like AI, software engineering, and machine learning is typically considered to be subfield of AI, but a lot of principles of software engineering apply in this area. This is what I want to talk about today. It's heavily used in data science. So, the difference between AI and data science is somewhat fluid if you like, but data science tries to understand what's in data and tries to understand questions about data. But then it tries to use this to make decisions, and then we are back at AI, artificial intelligence, where it's mostly about automating decision making. We have a couple of definitions. AI means using intelligence, making machines intelligent, and that means you can somehow function appropriate in an environment with foresight. Machine learning is a field that looks for algorithms that can automatically improve their performance without explicit programming, but by observing relevant data. And yes, I've thrown in data science as well for good measure, the scientific process of turning data into insight for making better decisions. If you have opened any newspaper, you must have seen the discussion around the ethical dimensions of artificial intelligence, machine learning or data science. Testing touches on that as well because there are quite a few problems in that space, and I'm just listing two here. So, you use data, obviously, to do machine learning. Where does this data come from, and are you allowed to use it? Do you violate any privacy laws, or are you building models that you use to make decisions about people? If you do that, then the general data protection regulation in the EU says you have to be able to explain to an individual if you're making a decision based on an algorithm or a machine, if this decision is of any kind of significant impact. That means, in machine learning, a lot of models are already out of the door because you can't do that. You can't explain why a certain decision comes out of a machine learning model if you use particular models.

A pioneering new clinical trial on a drug that could potentially help millions of men with prostate cancer, is under way in Derry. Tumour samples from men being treated locally are being collated to test the drug's effectiveness at the Clinical Transitional Research and Innovation Centre (C-TRIC), labs on the Altnagelvin Hospital site. The new trials are the result of a partnership with American pharmaceutical company Lantern Pharma and the PRAISE (prostate cancer artificial intelligence study using ex vivo models) trial is using artificial intelligence to test a cancer drug called LP-184 to predict which types of tumours are sensitive to it. The company said the groundbreaking work, which is partially funded by Invest NI, does not involve human or animals trials due to the use of AI. The new project will help guide future cancer research and clinical trials and early indications suggest there could also be benefits for research into the treatment of ovarian and liver cancer.

A pioneering new clinical trial on a drug that could potentially help millions of men with prostate cancer, is under way in Derry. Tumour samples from men being treated locally are being collated to test the drug's effectiveness at the Clinical Transitional Research and Innovation Centre (C-TRIC), labs on the Altnagelvin Hospital site. The new trials are the result of a partnership with American pharmaceutical company Lantern Pharma and the PRAISE (prostate cancer artificial intelligence study using ex vivo models) trial is using artificial intelligence to test a cancer drug called LP-184 to predict which types of tumours are sensitive to it. The company said the groundbreaking work, which is partially funded by Invest NI, does not involve human or animals trials due to the use of AI. The new project will help guide future cancer research and clinical trials and early indications suggest there could also be benefits for research into the treatment of ovarian and liver cancer.

Ott Velsberg, Estonia's fresh-faced, 28-year-old chief data officer, is on a mission put AI into every part of the country's public services, from healthcare to education and job centres. "The aim is to make government more proactive and responsive to people's life-events," says Velsberg. Instead of citizens having to apply for things like driver's licences and school places, he envisions a system where public bodies can anticipate and preemptively respond to the needs people have at different stages of their life. "We're not telling people what to do. That might happen in China but not in Estonia, or in Europe as a whole."

Over the past two decades, the philosopher David Chalmers has established himself as a leading thinker on consciousness. He began his academic career in mathematics but slowly migrated toward cognitive science and philosophy of mind. He eventually landed at Indiana University working under the guidance of Douglas Hofstadter, whose influential book "Gödel, Escher, Bach: An Eternal Golden Braid" had earned him a Pulitzer Prize. Chalmers's dissertation, "Toward a Theory of Consciousness," grew into his first book, "The Conscious Mind" (1996), which helped revive the philosophical conversation on consciousness. Perhaps his best-known contribution to philosophy is "the hard problem of consciousness" -- the problem of explaining subjective experience, the inner movie playing in every human mind, which in Chalmers's words will "persist even when the performance of all the relevant functions is explained."

Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups in Industry 4.0. As there is a large number of startups working on a wide variety of solutions, we decided to share our insights with you. This time, we are taking a look at 5 promising artificial intelligence (AI) solutions. For our 5 picks of artificial intelligence startups, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 5 interesting examples out of 214 relevant solutions. Depending on your specific needs, your top picks might look entirely different.

Years ago, a mobile app for email launched to immediate fanfare. Simply called Mailbox, its life was woefully cut short -- we'll get to that. Today, its founders are back with their second act: An AI-enabled assistant called Navigator meant to help teams work and communicate more efficiently. With the support of $12 million in Series A funding from CRV, #Angels, Designer Fund, SV Angel, Dropbox's Drew Houston and other angel investors, Aspen, the San Francisco and Seattle-based startup behind Navigator, has quietly been beta testing its tool within 50 organizations across the U.S. "We've had teams and research institutes and churches and academic institutions, places that aren't businesses at all in addition to smaller startups and large four-figure-person organizations using it," Mailbox and Navigator co-founder and chief executive officer Gentry Underwood tells TechCrunch. "Pretty much anywhere you have meetings, there is value for Navigator."

This seems like an obvious one, but with so many potential areas for AI exploration, starting with the right projects--and stakeholders--is crucial for long-term success. First and foremost, the process of identifying and selecting use cases shouldn't be driven by technology alone. That is, you don't want to think about AI solely in terms of where you can apply natural language processing, for example, or how you can leverage a labeled data set. Instead, ask where you seek to increase productivity or derive new value. Going through the questioning exercise above with the various leaders who may own or touch AI, such as the chief information officer, chief digital officer, chief data scientist, and other specialists (see #3), will enable you to identify where to start.