Modern social life often begins on screen. Digital profiles invite us to inspect the lives - and social circles - of friends, colleagues and strangers. Before meeting someone new, chances are we may have scanned their Instagram, LinkedIn or dating profile, forming assumptions from a carefully curated snapshot of their life. Somewhere along the way, we've forgotten the value of a considered, human introduction as the foundation for genuine connection. Bridget Jones's Shazza had it right when she said that making introductions with thoughtful details can go a long way: mentioning an unexpected talent or hobby, highlighting a mutual interest or sharing a funny anecdote.

Algorithms are a fundamental part of computer science, and they play a critical role in many areas of our lives. From search engines to social media, algorithms are used to sort and filter information, making it easier for us to find what we need. But what exactly are algorithms and how do they work? In this article, we will explore the basics of algorithms and their applications. An algorithm is a set of instructions that a computer can follow to perform a specific task.

The final round of this year's AI Song Contest was contested by an oddly dissonant ode to coffee, some boppy Eurovision-esque ditties, a gently French melody, and a host of more genre-defying anthems. The competition is styled on the Eurovision, although it is open to entries from around the world, and the songs are entirely composed by computers. Listening to the finalists, and the ultimate winner – Thailand's Yaboi Hanoi with Asura Deva Choom Noom (Enter Demons & Gods); at aisongcontest.com – I found myself wondering if anyone had cheated by adding a little helping human hand. I also found myself wondering where the boundaries blur. Machines have been facilitating music-making since instruments were invented, but computer technology is a seismic shift on a par with the advents of sound recording and electrical amplification. While AI is currently causing future-shock rumblings, the influence of computers on our music has been felt for a while.

Hardly a week goes by without a report announcing the end of work as we know it. In 2013, Oxford University academics Carl Frey and Michael Osborne were the first to capture this anxiety in a paper titled: "The Future of Employment: How susceptible are jobs to computerisation?". They concluded 47% of US jobs were threatened by automation. Since then, Frey has taken multiple opportunities to repeat his predictions of major labour market disruptions due to automation. In the face of threats to employment, some progressive thinkers advocate jettisoning our work ethic and building a world without work.

There's no existing classification for what makes a company an "AI company." Unfortunately for some, AI is just a buzzword that gets tacked onto marketing materials to attract funding. Startups are leveraging AI, and while this has brought some truly innovative solutions to the market, it's also been the spawning ground of a lot of fake AI startups. One of the most telling surveys of 2019 was conducted by London-based MMC Ventures, where it was revealed that 40% of Europe's AI startups are not using any AI at all. This might seem to be a controversial finding, but for many of us who work with AI on a daily basis, it's not particularly surprising.

Years ago, MIT Professor Neil Gershenfeld had an audacious thought. Struck by the fact that all the world's living things are built out of combinations of just 20 amino acids, he wondered: Might it be possible to create a kit of just 20 fundamental parts that could be used to assemble all of the different technological products in the world? Gershenfeld and his students have been making steady progress in that direction ever since. Their latest achievement, presented this week at an international robotics conference, consists of a set of five tiny fundamental parts that can be assembled into a wide variety of functional devices, including a tiny "walking" motor that can move back and forth across a surface or turn the gears of a machine. Previously, Gershenfeld and his students showed that structures assembled from many small, identical subunits can have numerous mechanical properties.

"Graphs have become very powerful, they're fundamental, and they're only becoming more and more important," said Dr. Steven Skiena, Director of the AI Institute at Stony Brook University. Graphs are a fundamental part of computer science, and they are only getting larger. Analyzing graphs using machine learning is powerful but requires translating them into numerical values. That's where graph algorithms come in. In this week's five-minute interview (conducted at GraphConnect 2018 in NYC), we spoke with Dr. Steven Skiena, author of The Algorithm Design Manual and The Data Science Design Manual, about his work on DeepWalk, slated to be incorporated into the Neo4j graph algorithm library.

Artificial intelligence (AI) and machine learning technologies have turned the IT industry on its head, offering enterprises the ability to transform their approach to business strategy and customer insights, and research institutions to pursue humanity's biggest challenges. Once considered an abstract technology that was primarily championed by hyperscale companies (like Google, Microsoft, Baidu etc), it is encouraging to now see startups and larger enterprises alike across a variety of industries explore unique AI applications to solve business problems and scientific challenges. From assisting in healthcare diagnoses, to predicting when things like a jet engine is in need of maintenance, and assisting in crime prevention, the potential for innovation with AI is nearly endless. It's even touching the average consumer's daily life, as well: Facebook's suggested photo tagging feature, for example, uses AI to recognize who's who in your pictures. Of course, AI does have its pain points and a key challenge for businesses today is the ability to differentiate between what's hype and what's reality.

In 1965, I. J. Good described for the first time the notion of "intelligence explosion", as it relates to artificial intelligence (AI): Decades later, the concept of an "intelligence explosion" -- leading to the sudden rise of "superintelligence" and the accidental end of the human race -- has taken hold in the AI community. Famous business leaders are casting it as a major risk, greater than nuclear war or climate change. Average graduate students in machine learning are endorsing it. In a 2015 email survey targeting AI researchers, 29% of respondents answered that intelligence explosion was "likely" or "highly likely". A further 21% considered it a serious possibility.

In 1965, I. J. Good described for the first time the notion of "intelligence explosion", as it relates to artificial intelligence (AI): Decades later, the concept of an "intelligence explosion" -- leading to the sudden rise of "superintelligence" and the accidental end of the human race -- has taken hold in the AI community. Famous business leaders are casting it as a major risk, greater than nuclear war or climate change. Average graduate students in machine learning are endorsing it. In a 2015 email survey targeting AI researchers, 29% of respondents answered that intelligence explosion was "likely" or "highly likely". A further 21% considered it a serious possibility.