AI milestones in 2020
10 documented milestones from 2020.
Milad Abolhasani and colleagues demonstrated Artificial Chemist 2.0, an autonomous flow chemistry platform for quantum dot synthesis
In December 2020, Milad Abolhasani's group at North Carolina State University published Artificial Chemist 2.0, an autonomous flow chemistry system combining machine learning with robotic synthesis to navigate a space of approximately 20 million quantum dot formulations and produce a target material within roughly 30 minutes of initiating a search.
Soft Robotic Gripper Modelled on Pole Bean Tendrils Developed at University of Georgia
In December 2020, researchers at the University of Georgia developed a soft robotic gripper modelled on the twining behaviour of pole bean tendrils. The 3-inch device uses a single pneumatic actuator and an embedded fibre-optic sensor to grasp objects as small as 1 millimetre in diameter and characterise surface properties during contact.
DeepMind's AlphaFold 2 Achieves Highest-Accuracy Results at CASP14 Protein Structure Prediction Competition
In November–December 2020, DeepMind's AlphaFold 2 system achieved a median Global Distance Test score of approximately 92.4 across all CASP14 targets, far surpassing the next-best group, in a result that computational biologists described as largely solving the 50-year-old protein-folding problem for single-chain proteins.
Waymo One Launches Fully Driverless Rides to the General Public in Phoenix, Arizona
On 8 October 2020, Waymo opened its Waymo One ride-hailing service to the general public in the greater Phoenix, Arizona area, operating without a safety driver in the vehicle, the first time a commercial autonomous vehicle service had done so at public scale.
CURIAL: An AI System to Detect COVID-19 in Emergency Department Patients Using Routine Blood Tests and Vital Signs
In July 2020, researchers at the University of Oxford, led by Dr Andrew Soltan and Professor David Clifton, announced CURIAL, a machine-learning model trained on routine blood tests and vital signs from 115,000 hospital presentations that could identify COVID-19 patients in emergency departments within one hour and with accuracy exceeding 90%.
OpenAI released GPT-3 via private beta API
In May–June 2020, OpenAI published the GPT-3 language model in a paper by Tom B. Brown and colleagues, and began distributing private beta API access. GPT-3's 175 billion parameters made it substantially larger than any publicly described language model at the time, enabling strong few-shot performance across diverse language tasks.
Once-for-All: Train One Network and Specialize It for Efficient Deployment
Han Cai, Chuang Gan, Tianhao Chen, and Song Han at MIT published Once-for-All at ICLR 2020, presenting a method to train a single neural network once and then derive specialised sub-networks for diverse hardware platforms without retraining, reducing the computational cost of neural architecture search by orders of magnitude.
MIT Researchers Use Machine Learning to Identify Halicin, an Antibiotic Effective Against Drug-Resistant Bacteria
On 20 February 2020, James Collins and colleagues at MIT published research in Cell describing a deep-learning model trained to predict antibiotic activity; the model identified halicin, a compound previously investigated for diabetes treatment, as a potent broad-spectrum antibiotic capable of killing several drug-resistant bacterial strains.
Microsoft Research released Turing Natural Language Generation (T-NLG), a 17-billion-parameter language model
In February 2020, Microsoft Research announced Turing Natural Language Generation (T-NLG), a 17-billion-parameter autoregressive language model trained using the Megatron-LM framework. At the time of release it was the largest publicly disclosed language model and achieved state-of-the-art results on question-answering and summarisation benchmarks.
Facebook AI Research Publishes GrokNet, a Unified Computer Vision Model for Commerce Understanding
In 2020, researchers at Facebook AI published GrokNet, a unified deep learning system for product understanding in commerce settings, capable of recognising object categories, attributes such as colour and material, and brand information from product images at scale across Facebook Shops.