AI News, neomatrix369/awesome artificial intelligence
Two years in the life of AI, ML, DL and Java
One of my motivations of putting this post and the links below together comes from the discussion we had during the LJC Unconference in November 2018, where Jeremie, Michael Bateman and I along with a number of LJC JUG members gathered at a session discussing a similar topic.
And in case you feel further improvements can be made to the content, categorisation, layout, please feel free to contribute, you can start by visiting the git repo and creating a pull request.
Here’s a number of resources shared in the last two years (circa), categorised as best I could: Due to a large number of the links gathered, not all of them could be shown here and so I have created a git repo and to host them on GitHub, where you will find the rest of the links.
From my several weeks to few months of intense experience I suggest if you want to get your hands dirty with Artificial Intelligence and it’s off-springs , don’t shy away from it, just because it is not Java / JVM based.
Also, would like to thank the good folks (Helen and team) behind the ML Study group in London — supported by @RWmeetamentor, who have been working hard to bring everyone together to learn ML and related topics.
I know it is not going to make me popular by saying this but my humble request to all developers would be that not to think or expect everything possible from a single programming language.
The Most Amazing Artificial Intelligence Milestones So Far
Artificial Intelligence (AI) is the hot topic of the moment in technology, and the driving force behind most of the big technological breakthroughs of recent years.
Since the dawn of computing in the early 20th century, scientists and engineers have understood that the eventual aim is to build machines capable of thinking and learning in the way that the human brain – the most sophisticated decision-making system in the known universe – does.
1956 – The Dartmouth Conference With the emergence of ideas such as neural networks and machine learning, Dartmouth College professor John McCarthy coined the term 'artificial intelligence' and organized an intensive summer workshop bringing together leading experts in the field.
ELIZA represented an early implementation of natural language processing, which aims to teach computers to communicate with us in human language, rather than to require us to program them in computer code, or interact through a user interface.
However, it was important from a publicity point of view – drawing attention to the fact that computers were evolving very quickly and becoming increasingly competent at activities at which humans previously reigned unchallenged.
This was significant because while Deep Blue had proven over a decade previously that a game where moves could be described mathematically, like chess could be conquered through brute force, the concept of a computer beating humans at a language based, the creative-thinking game was unheard of.
2012 – The true power of deep learning is unveiled to the world – computers learn to identify cats Researchers at Stanford and Google including Jeff Dean and Andrew Ng publish their paper Building High-Level Features Using Large Scale Unsupervised Learning, building on previous research into multilayer neural nets known as deep neural networks.
2015 – Machines “see” better than humans Researchers studying the annual ImageNet challenge – where algorithms compete to show their proficiency in recognizing and describing a library of 1,000 images – declare that machines are now outperforming humans.
Since the contest was launched in 2010, the accuracy rate of the winning algorithm increased from 71.8% to 97.3% - promoting researchers to declare that computers could identify objects in visual data more accurately than humans.
2016 – AlphaGo goes where no machine has gone before Gameplay has long been a chosen method for demonstrating the abilities of thinking machines, and the trend continued to make headlines in 2016 when AlphaGo, created by Deep Mind (now a Google subsidiary) defeated world Go champion Lee Sedol over five matches.
Although Go moves can be described mathematically, the sheer number of the variations of the game that can be played – there are over 100,000 possible opening moves in Go, compared to 400 in Chess) make the brute force approach impractical.
While human operators currently ride with every vehicle, to monitor their performance and take the controls in case of emergency, this undoubtedly marks a significant step towards a future where self-driving cars will be a reality for all of us.