After the'top AI books' reading list was so well received, we reached out to some of our community to find out which papers they believe everyone should have read! All of the below papers are free to access and cover a range of topics from Hypergradients to modeling yield response for CNNs. Each expert also included a reason as to why the paper was picked as well as a short bio. We spoke to Jeff back in January and at that time he couldn't pick just one paper as a must-read, so we let him pick two. This paper unpacks two key talking points, the limitations of sparse training data and also if recurrent networks can support meta-learning in a fully supervised context.

Microsoft's AI ethics committee helped craft internal Department of Defense contract policy, and G20 member nations wouldn't have passed AI ethics principles if it weren't for Japanese leadership. Published Tuesday, the UC Berkeley Center for Long-Term Cybersecurity (CLTC) case study examines how organizations are putting AI ethics principles into practice. Ethics principles are often vaguely phrased rules that can be challenging to translate into the daily practices of an engineer or other frontline worker. CLTC research fellow Jessica Cussins Newman told VentureBeat that many AI ethics and governance debates have focused more on what is needed, but less on the practices and policies necessary to implement goals enshrined in principles. The study focuses on OpenAI's rollout of GPT-2; the adoption of AI principles by OECD and G20; and the creation of the AI, Ethics, and Effects in Engineering and Research (AETHER) committee at Microsoft.

Sponsored Everyone seems alive to the potential of Artificial Intelligence in business and the public sector. According to research from PwC, worldwide we can expect to see a boost to economies of $15.7tn by 2030 as more organisations unlock new opportunities in big data and advanced analytics in such fields as financial services, retail, transport and government. The sweet spot of AI is the potential scale of data processing at a volume beyond human capabilities – combined with the capacity for systems to learn and develop unprompted responses. But while PwC's numbers are impressive – and explain why so many are keen to start big-data and AI initiatives – it's important to remember we're still in a very early stage of deployment. "We have to think that AI is still a teenager," says Walter Riviera, EMEA AI Technical Engineer at Intel .

Covariant today announced the close of a $40 million series B funding round to bring its robotic control systems to additional industries and create more systems capable of picking, placing, and unloading objects in warehouses. Until now, Covariant has focused its efforts on ecommerce picking robots in highly automated warehouses. It may be best known for its work in robotic grasping, the task of picking up objects with a robotic hand or gripper. The startup -- whose founders who met at OpenAI and University of California, Berkeley -- has raised $67 million, to date. After emerging from stealth earlier this year with support from deep learning luminaries like Geoffrey Hinton, Jeff Dean, and Yann LeCun, Covariant stated that the Covariant Brain system is capable of picking and packing some 10,000 items with 99% accuracy. Robotics manufacturer ABB signed a partnership with Covariant in February, following a picking and sorting test held by ABB last year in which Covariant outperformed 20 other systems.

Machine learning algorithms have been available since the 1990s, but it is much more recently that they have come into use also in the physical sciences. While these algorithms have already proven to be useful in uncovering new properties of materials and in simplifying experimental protocols, their usage in liquid crystals research is still limited. This is surprising because optical imaging techniques are often applied in this line of research, and it is precisely with images that machine learning algorithms have achieved major breakthroughs in recent years. Here we use convolutional neural networks to probe several properties of liquid crystals directly from their optical images and without using manual feature engineering. By optimizing simple architectures, we find that convolutional neural networks can predict physical properties of liquid crystals with exceptional accuracy.

The area of AI (Artificial Intelligence) has seen breaking-throughs in deep learning.Approximately 100 billion cells in the human brain are called neurons. This creates massively parallel and centralized networks that allow us to learn and perform complex activities. Based on these biological neural nets, scientists have started developing artificial neural networks to eventually allow computers to learn and view intelligence like people. FLIR Systems' deep education evangelist, "These data are used to teach the neural network itself to learn what is good or bad. You can, for instance, display pictures of fruits labeled'Grade A' or'Grade B,' 'Grade C,' or so on if you wish to make the neural network fruit.

Artists may need to start competing with -- or embracing -- computer-made songs and soundtracks in the near future, if a new AI music generator shows any indication of what could come next for the music industry. Researchers at artificial intelligence lab OpenAI have released Jukebox, an open-source algorithm that can generate music, complete with lyrics, vocals, and a soundtrack. All the algorithm needs is a genre, an artist, and a snippet of lyrics, and Jukebox can create song samples that can be realistic and quite catchy. OpenAI's music generator runs on the same sort of machine-learning technology used to create deepfakes and employed by the slew of sites that popped up in 2019 generating fake memes, fake Airbnb listings, and fake cats. Jukebox produces its AI creations using artificial neural networks that train a computer to learn from an influx of data.

Deep learning models have brought great success to NLP applications thanks to the untiring efforts of the ML community to improve the accuracy of these models. These improvements, however, come at a cost. The computational resources required and the time consumed add up to the overall tweaking of the model. NLP models especially, have become quite popular with Microsoft, Google and NVIDIA releasing large models in the past couple of years. But how much does training these models cost is rarely talked about.

For the purpose of this narrative review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and medicine and cardiology subspecialties. Articles were reviewed and selected for inclusion on the basis of relevance. This article highlights that the role of ML in cardiovascular medicine is rapidly emerging, and mounting evidence indicates it will power the new tools that drive the field. Among other uses, AI has been deployed to interpret echocardiograms, to automatically identify heart rhythms from an ECG, to uniquely identify an individual using the ECG as a biometric signal, and to detect the presence of heart disease such as left ventricular dysfunction from the surface ECG.6x6Attia, Z.I., Kapa, S., Lopez-Jimenez, F. et al.

Imagine a high-security complex protected by a facial recognition system powered by deep learning. The artificial intelligence algorithm has been tuned to unlock the doors for authorized personnel only, a convenient alternative to fumbling for your keys at every door. A stranger shows up, dons a bizarre set of spectacles, and all of a sudden, the facial recognition system mistakes him for the company's CEO and opens all the doors for him. By installing a backdoor in the deep learning algorithm, the malicious actor ironically gained access to the building through the front door. This is not a page out of a sci-fi novel.