IBM and the Quebec AI Institute (Mila) are collaborating to accelerate the Orion AI and machine learning open source technology they started working on together in early 2020, to improve a key component known as hyperparameter optimization. This tuning is to set rules used to control the learning process. The values of parameters can be referred to also as node weights. The project aims to help researchers improve machine learning model performance, and pinpoint with the “black box” of AI where their models need work, according to a related IBM press release .
The Orion software (no relation to the recently-hacked SolarWinds Orion platform) is envisioned as a backend to complement existing machine learning frameworks, according to an account from Mila.
“The goals of this project are to 1) create a tool well-adapted to researchers’ workflow and with little configuration/manipulation required, 2) establish clear benchmarks to convince researchers of efficiency, and 3) leverage prior knowledge to avoid optimization from scratch,” stated Xavier Bouthillier, lead developer of Orion and a Phd computer science student at the University of Montreal.
Mila and IBM have built a benchmarking module in Orion, with a variety of assessments and tasks to ensure sufficient coverage of most use cases encountered in research. For each task, optimization algorithms can be benchmarked based on various assessment scenarios. These include: time to result, average performance, search space dimensions size, search space dimensions type, parallel execution advantage, and search algorithm parameters sensitivity. IBM Intends to Integrate Orion Code into Watson Machine Learning Accelerator
IBM’s Spectrum Computing team based in Markham, Ontario, has contributed to the Orion code base. IBM intends to integrate the open-source Orion code into its Watson Machine Learning Accelerator.
Yoshua Bengio, Scientific Director at Mila and one of the world’s leading experts in artificial intelligence and deep learning, stated, “A collaboration with leading industry AI experts such as IBM is a great opportunity to accelerate the development of an open-source solution recently initiated at Mila, combining engineering expertise, practical hands-on experience and cutting-edge research in AI.”
Bengio added, “Hyperparameter optimization plays an important role in the scientific progress of AI, both as an enabler to reach the best performances achievable by new algorithms, and as a foundation for a rigorous measure of progress, providing a principled common ground to compare algorithms. Hyperparameter optimization and its subfield of neural architecture search are additionally a key solution for the deployment of energy-efficient AI technologies, a problem currently posed by the trend of increasing computational cost of deep learning models.”
How AI is Helping with COVID-19 Vaccine Rollout and Tracking
AI has been employed since the early days of the COVID-19 pandemic to track the spread of positive cases, to crunch through thousands of scientific papers to search for treatment options and to help develop a vaccine. Now AI and other digital tools are being deployed to manage complex supply chains for the vaccine.
With the third highest number of coronavirus cases in Europe after France and Italy, according to data from Johns Hopkins University, the UK is the first country to distribute the Pfizer vaccine. The UK has 1.7 million confirmed cases in a population of close to 68 million people, Tracking side effects from the vaccine rollout is a huge task, UK health officials have said, according to an account from Nasdaq. To help meet the challenge, the UK Medicines & Healthcare products Regulatory Agency (MHRA) recently partnered with the UK unit of Genpact, the global professional services firm specializing in digital transformation. The company is integrating components of its AI software suite with the government’s website where adverse effects are reported. “When a vaccine gets distributed at scale and speed, a technology solution needs to track the batch and lot numbers to know exactly where each dose is and who received it,” stated Eric Sandor, drug safety AI lead at Genpact. “There’s a lot of information, in a number of different formats, and it’s very manually intensive to try to codify it in a way that makes sense. AI will help with processing all that data faster than humans can. It’s quite complicated at scale, but is a critical element to overall public health.”
With the Genpact AI, the UK government will be able to track events by batch, lot, and location so that any adverse effects can be reported back to the drug manufacturers. The technology will also track issues or trends related to ethnicity, age, gender, or other demographic factors that can come into play with the vaccine. “Humans are good at balancing about seven different dimensions of data before we sort of run out of road,” stated Sandor. “AI can handle thousands of dimensions of data and find patterns and signals in the data very rapidly, something that would take humans much longer to find.”
Pharmaceutical companies are also just beginning to explore the role of AI in supply chain management, another challenge of the vaccine rollout. The procurement, delivery logistics, tracing and storage all affect the availability of the vaccine and are potential risk factors for private companies. Genpact has been preparing. “We’re running AI applications for clients that model the distribution of the vaccine and how that’s going to work not only for pharmaceuticals, but for other companies in the life sciences space,” stated Katie Stein, chief strategy officer for Genpact.