Reports indicate a 1 in 10 success rate.
Not great.
So AutoML will be in demand in 2020, although I personally think, like making great search results, successful AI requires custom solutions specific to the business.
Ines Montani (@_inesmontani) is a software developer working on Artificial Intelligence and Natural Language Processing technologies, and the co-founder of Explosion.
Everyone is opting for “DIY AI” instead of cloud solutions.
One factor driving this trend is the success of transfer learning, which has made it easier for anyone to train their own models with good accuracy, fine-tuned to their very specific use case.
With one user per model, theres no real economy of scale for a service provider to exploit.
Another advantage of transfer learning is that datasets dont need to be as large anymore, so annotation is moving in-house as well.
The in-housing trend is a positive development: commercial AI is a lot less centralized than many people thought it would be.
A few years ago, people worried that everyone would get “their AI” from just one provider.
Instead people arent getting their AI from any provider – theyre doing it themselves.
Dipanjan Sarkar is Data Science Lead at Applied Materials, a Google Developer Expert – Machine Learning, an author, consultant, and trainer.
The major advancements in the world of Artificial Intelligence in 2019 have been in the areas of Auto-ML, Explainable AI and Deep Learning.
Democratization of Data Science remains a key aspect since the last couple of years and various tools and frameworks pertaining to Auto-ML are trying to make this easier.
The caveat still remains that we need to be careful when using these tools to make sure we don’t end up with biased or overfit models.
Fairness, accountability and transparency still remain key factors for customers, businesses and enterprises to accept decisions made by AI.
Hence Explainable AI is no longer a topic just restricted to research papers.
A lot of excellent tools and techniques have started making machine learning model decisions more interpretable.
Last but not the least, we have seen a lot of progress in the world of Deep Learning and Transfer Learning especially for Natural Language Processing.
I expect to see more research and models coming up in 2020 around areas of Deep Transfer Learning for NLP and Computer Vision and hopefully something which looks at taking the best of Deep Learning and Neuroscience which can lead us towards true AGI.
Elena Sharova is Senior Data Scientist at ITV.
By far the most important ML developments of 2019 were made with deep Reinforcement Learning in playing games with DeepMind DQN and AlphaGo; leading to the retirement of the Go champion Lee Sedol.
Another important advance was in natural language processing with BERT (deeply bidirectional language representation) being open-sourced by Google and Microsoft leading the GLUE benchmark with the development and open sourcing of the MT-DNN ensemble for pronounce resolution tasks.
It is important to highlight the European Commission’s publication of Ethics Guidelines for Trustworthy AI – the first official publication setting out sensible guidelines for lawful, ethical and robust AI.
Finally, I am sharing with KDnuggets readers that all the keynote speakers at PyData London 2019 were women – a welcome development!I expect that the main ML development trends of 2020 will continue within NLP and computer vision.
Industries adopting ML and DS have realised that they are overdue defining shared standards for best practices in hiring and retaining data scientists, managing the complexity of projects that involve DS and ML, and ensuring the community remains open and collaborative.
Thus we should see more focus placed on such standards in the near future.
Rosaria Silipo (@DMR_Rosaria) is Principal Data Scientist at KNIME.
The most promising achievement in 2019 has been the adoption of active learning, reinforcement learning, and other semi-supervised learning procedures.
Semi-supervised learning might offer a hope to take a stub at all these unlabelled data currently populating our databases.
Another great advancement has been the correction of the word “auto” with “guided” within the autoML concept.
Expert intervention seems to be indispensable for more complex Data Science problems.
In 2020, data scientists will require a rapid solution for easy model deployment, constant model monitoring, and flexible model management.
Real business value will derive from these three final parts of the Data Science life-cycle.
I also believe that a more extensive usage of deep learning black-boxes will raise the problem of Machine Learning Interpretability (MLI).
We will see at the end of 2020 whether MLI algorithms are up to the challenge of explaining exhaustively what is going on behind closed doors of a deep learning model.
Daniel Tunkelang (@dtunkelang) is an independent consultant specializing in search, discovery, and ML/AI.
The cutting edge of AI continues to be focused on language understanding and generation.
OpenAI announced GPT-2 to predict and generate text.
OpenAI did not release the trained model at the time, out of concern for malicious applications, but they eventually changed their mind.
Google released an on-device speech recognizer that fits in 80MB, making it possible to perform speech recognition on mobile devices without sending data to the cloud.
Meanwhile, were seeing a crescendo of concern around AI and privacy.
This year, all of the major digital assistant companies faced backlash around employees or contractors listening to users conversations.
What does 2020 have in store for AI?.Well see further advances in conversational AI, as well as better generation of images and video.
Those advances will raise even greater concerns around malicious applications, and well probably see a scandal or two, especially in an election year.
The tension between good and evil AI isnt going away, and well have to learn better ways to deal with it.
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