March brought interesting publications about the limited ability of machines to read Bengali script, the challenges of generalization for machines and new materials discovered by neural networks.
These issues are connected by one crucial aspect – they are challenges for today’s AI, and come with massive consequences, from limiting the survivability of language to saving human lives and pushing progress forward.
Pushing to make Bengali script comprehensible for machines
AI-based tools not only reduce the number of mundane and boring tasks that fall to humans in daily business operations but also help to categorize documents and digitize what hasn’t yet been automated. Optical Character Recognition (OCR) recognizes printed text well when it’s done in the Latin alphabet, but it apparently fares far worse in reading the Bengali alphabet, which has 50 letters, 11 vowels and 39 consonants and is the sixth most used writing system in the world.
To tackle this challenge, a group of researchers from United International University in Bangladesh cataloged the challenges OCR faces in reading Bengali.
Why it matters
OCR technology makes digitizing printed materials–books, newspaper and old manuscripts alike–easier. It is a self-feeding mechanism, where the amount of digital texts serves as the basis for training natural language processing models. With those, AI can provide more support in performing duties in a language, so digitizing more texts widens the world of possibilities a language offers.
Also, when a language is harder for AI solutions to understand, it is less likely to be applied in new solutions, thus making solutions designed to work for that language ineffective, be that a recommender engine or other tool.
In the long term, this could result in a lower number of speakers of the language, and thus throw the language into decline, and with it that little bit of the world’s cultural heritage the language represents. So in fact, working to make the language comprehensible for machines can be seen as a struggle to keep it alive in the future.
Machine learning tackles antibiotic resistance
Antibiotics are among the most significant achievements in medicine, saving the lives of thousands of people around the world and effectively ending the era of widespread death due to common infections. Apparently that era is on its way back.
According to a WHO report on antimicrobial resistance, drug-resistant diseases already cause at least 700,000 deaths globally a year, including 230,000 deaths from multidrug-resistant tuberculosis. In the most pessimistic scenario, in the absence of action, that figure could increase to 10 million deaths globally per year by 2050.
This information is even more alarming in the light of the COVID-19 outbreak, a bacterial infraction after viral infections (especially respiratory system-effecting ones) are increasing the death toll and make the overall disease more severe.
Multiple new chemicals would be viable in antibiotic treatments, but they are the proverbial needle in a haystack. Harnessing the power of deep neural networks can make searching for them easier and faster by tossing out the limits of human perception.
An example of this approach can be found in this recent article in Cell magazine.
Why it matters
AI is a revolutionary technology that supports a massive variety of activities — in business by delivering better demand forecasting, in security with AIOps platforms and automated network traffic analytics and in manufacturing with quality control.
Using neural networks to improve healthcare is an effective way to make lives easier, increase longevity and enhance the quality of human life globally.
Using AI to deliver new materials for batteries
Harnessing neural networks in the search for new materials goes well beyond drug research. In fact, the need for new, more resilient, flexible and effective materials extends to nearly all industries. The revolution in IoT, wearables and smart appliances and electric cars is powering (pun unintended) the need for new materials for batteries.
To see how neural networks are being used to search for new materials for batteries, see this article in MIT News.
Why it matters
Human civilization is highly dependent on electricity and the ability to store it efficiently is crucial for our further development. Researching new batteries and materials to build them will (again – pun intended) power our further progress.
Can AI generalize? Apparently not
The ability to use a skill to do something new or in a new environment is a mark of intelligence, both natural or artificial. The concept of generalization is simple, yet there was no repeatable and reliable way to test if a system can do it.
This led a consortium of researchers from the University of Amsterdam, MIT, ICREA, Facebook, and NYU to produce gSCAN, a benchmark for generalization abilities. The tests they produced are simple, at least from a human point of view: drive in a direction never before taken (for example turning right when taught to always turn left).
Details about the benchmark can be found in this Arxiv paper.
Why it matters
The ability to generalize is the next step in the development of AI. The benchmark tests function similarly to the Atari games used in reinforcement learning – a technique limited enough to be applied swiftly, but applicable upon extrapolation in more sophisticated projects.
Also, the ability to generalize marks a limit of AI, which needs to be pushed forward to deliver new results. This benchmark is a way to do that, or at least test if it’s possible.