IBM, Daimler AG, and Virginia Tech researchers simulate materials with fewer qubits. Their work was published in the October issue of the Royal Society of Chemistry’s journal, “Physical Chemistry, Chemical Physics” and featured as a “hot topic” in 2020.
“Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical., and by golly, it’s a wonderful problem because it doesn’t look so easy.” – Richard P. Feynmann.
The above quote from the great physicist reminds us of the limitations of classical computers to simulate nature. It is indeed the most suited problem for quantum computers to simulate intrinsically quantum mechanical systems like molecules more efficiently than classical computers.
Quantum computers can predict the properties of molecules with precision on par with actual lab experiments. It involves accurate modeling of molecules of a compound and the particles that make up these molecules to simulate how they react in many different environments. There are infinite molecular combinations to test before the right one is found, requiring large numbers of qubits and quantum operations.
In practice, there are two approaches for simulating with fewer quantum resources. One approach is to perform classically intractable calculations on the quantum computer followed by classical post-processing to correct for basis set errors associated with using fewer qubits. The second is to reduce the quantum resources required for more accurate calculations — the number of qubits and quantum gates. The researchers adopted the latter approach.
The information density of negatively charged electrons to repel each other does not usually fit existing quantum computers because it requires too much extra computation. Therefore, the researchers incorporated electrons’ behavior directly into a transcorrelated Hamiltonian. The result was an increased simulation’s accuracy without the need for more qubits.
Daimler, an IBM’s research partner, has heavily invested in designing better batteries for electric vehicles that will occupy tomorrow’s roads. The company wants to build the capacity to search for new materials that can lead to higher-performing, longer-lasting, and less expensive batteries. Therefore, Daimler intends to simulate more and more orbitals for reproducing the results of an actual experiment as better modeling and simulations will ultimately result in the prediction of new materials with specific properties of interest.
Neuroscientists recently showed that the brain’s storage scheme is more capable of storing information than the neural networks. The paper from neuroscientists of SISSA, in collaboration with Kavli Institute for Systems Neuroscience & Norway’s Centre for Neural Computation, featured in the prestigious Physical Reviews Letters.
The basic unit of neural networks are neurons that learn patterns by fine-tuning the connections among them. The stronger the connections, the lesser is the chance to overlook any pattern. Neural networks use the backpropagation algorithms to tune and optimize the connections during the training phase iteratively. In the end, the neurons recognize patterns by the mapping function they have approximated, i.e., network memory.
This procedure works well in a static setting where no new data is being ingested. In a continual environment, where the models learn new patterns across diverse tasks over extended periods like humans, the neural networks suffer from catastrophic forgetting. So, there must be something else that makes the brain much more powerful and efficient.
The answer is in the brain’s more straightforward approach: the link between neurons decides how the pattern changes. Scientists thought that the simpler process would permit fewer memories based on the fundamental assumption that neurons are binary units. But the researchers showed that the fewer memories are the result of using such an unrealistic assumption. They combined the brain’s storage scheme to change the connections with biologically plausible models for single neurons response and found that the hybrid performs at par and beyond AI algorithms.
The researchers pinpointed the role of introducing errors in the performance boost. Usually, when the brain retrieves a memory correctly, it will be identical to the original input-to-be-memorized memory or correlated to it. But the brain storage scheme retrieves memories that are not identical to the initial input.
The neurons that are barely active in memory retrieval and do not distinguish among the different memories stored within the same network are silenced. These freed neural resources are focused on those neurons that matter in an input to be memorized and lead to a higher memory capacity. It is believed that the recent findings shall seep into the field of continual learning and multitask learning to produce more robust neural models that can handle catastrophic forgetting.
FEderated LearnIng with a CentralIzed Adversary (FELICIA) — a federated generative mechanism enabling collaborative learning — has been recently proposed by researchers from Microsoft and the University of British Columbia to train models from private medical data.
What is the problem?
There has been an outcry among AI researchers to gain easier medical data access from varied sources to better train medical diagnosis models like disease detection and biomedical segmentation. Biased by the demographics, medical equipment types, and acquisition process, images from a single source would skew any models’ performance towards the source population. The model would then perform poorly for other populations.
Therefore, medical data owners, such as hospitals and research centers, share their medical images to access differently sourced data and cut their data curation costs. They mostly use the additional data to counter the bias arising from their limited data while keeping source data private from others. But the legal constraints complicate the access to external large medical datasets. Current legislation prevents the sharing and processing of datasets outside the source from avoiding privacy breaches. Thanks to lower data diversity involved in diagnostics, the very laws that safeguard patients’ privacy endanger their lives because of less powerful AI models.
Therefore, the researchers generate synthetic medical data to set the data imbalance right, using Generative Adversarial Networks (GANs) architectures to train models. GANs have two neural networks — adversaries — competing against each other. While one of the networks is a generator that produces fake data as real as possible, the other is a discriminator that discriminates between the fake and real data from the mixed input. The generated data is mixed with the real ones. In a zero-sum-game, both try harder and harder to beat each other. And the result is a generator network that can generate fake data closer to real ones.
What does the best solution look like?
The best solution is to build upon PrivGAN architecture that works locally on a single dataset and generates synthetic images. But another group of researchers showed that PrivGAN could be used in a federated learning setting. PrivGAN was designed to protect against membership inference attacks — noticeable patterns in outputs that leak training data. This robustness against training data leakage makes PrivGAN the candidate for Microsoft’s FELICIA that honors medical data privacy constraints.
What is the best way to implement the solution?
Microsoft’s FELICIA simply extends any GAN to a federated learning setting using a centralized adversary, a central discriminator with limited access to shared data. The sharing of data with the central discriminator depends on many factors such as use cases, regulation, business value protection, and infrastructure. Researchers used multiple copies of the same discriminator and generator architectures of a ‘base GAN’ inside FELICIA to test the mechanism. The central privacy discriminator (DP) is kept identical to the other discriminators except for the final layer’s activation. First, the base GANs are trained individually on the whole training data to generate realistic images. Then FELICIA’s parameters, jointly optimized using the base GANs parameters, are tuned to get real-like synthetic samples.
FELICIA’s federated loss function equally optimizes the local utility on local data and global utility on all users’ data. It means that successive synthetic images will have to be far better than the previous ones, both at the local and global levels. The hyperparameter λ, which balances the participation in global loss optimization, improves the utility contrary to the original PrivGAN loss.
Did Microsoft’s FELICIA work?
Yes, FELICIA’s images are clearer and more diverse than other GANs. It generates synthetic images with more utility than what is possible with only access to local images. The improved utility suggests that the samples cover more of the input space than those of the local GANs.
During multiple experiments, it was seen that combining FELICIA with real data achieves performance on par with real data while most results significantly improve the utility even in the worst cases. The improvement is particularly significant when the data is most biased. The more biased the dataset is, the more its synthetic data will benefit in utility. Excluding the first 10000 epochs, a FELICIA augmented dataset is almost always better than what is achieved by real images.
These results show that Microsoft’s FELICIA allows the owner of a rich medical image set to help an owner of a small and biased set of images to improve its utility while never sharing any image. Different data owners (e.g., hospitals) now could help each other by creating joint or disjoint synthetic datasets that contain more utility than any of the single datasets alone. Such a synthetic dataset could be instrumental to freely share images within the local hospital and keep the real images secured and available to a limited number of individuals. This arrangement produces powerful models trained with shared data among research groups while maintaining confidentiality measures. A data owner can generate high-quality synthetic images with high utility while providing no access to its data.
Google Cloud introduces Skills Challenge that will offer free training to learners on Google Labs. The initial four tracks in the skills challenge include getting started, machine learning and artificial intelligence, data analytics, and hybrid and multi-cloud. Learners can also gain skill badges to demonstrate their expertise in the latest technologies on social and professional media platforms.
With Google Cloud Skills Challenge, learners can master skills to create machine learning models, deploy virtual machines, run applications on Kubernetes, manage cloud resources, use machine learning APIs, setup and configure cloud environments, and more.
One of the biggest trends in 2021 is to obtain skills to use the latest technologies like AI, ML, and software development on the cloud. The pandemic has made cloud computing skills a necessity for aspirants as well as practitioners.
Companies are highly relying on cloud computing since the pandemic caused by covid-19. As more professionals are working from home, the cloud has become the go-to platform for all data, AI, and software development tasks.
Google Cloud Skills Challenge brings an opportunity for learners to gain the most in-demand skills for free. The last date to register for the Google Cloud Skills Challenge is by January 31, 2021. Since each of the challenges can be completed in one month, Google Cloud is offering this free training for 30 days.
Over the last few months, Google Cloud has been providing several opportunities for learners to master new skills. Late last year, Google Cloud offered free training through its Qwiklabs. Being one of the top three cloud providers, Google Cloud skills can help in career advancements to technology enthusiasts.
The researchers from Adobe and Auburn University pointed out that current BERT-based language models are simply deep n-gram models because they blatantly reject taking word order into account.
In natural language, word order remains highly constrained by many linguistic factors, including syntactic structures, subcategorization, and discourse. Arranging words in the correct order is considered a critical problem in language modeling. In earlier times, statistical models like n-grams were used for primitive Natural Language Understanding (NLU) tasks, like sentiment analysis, sentence completion, and more. But those models have many problems like being ineffective at preserving long-term dependencies, loss of context, and sparsity. They can not produce convincible long sentences in the correct word order. Thanks to attention modules, feats achieved by many language models like Microsoft’s DeBerta, GPT-3, and Google’s Switch-Transformers made us believe that the word-order problem is solved for good.
Sadly, the researchers found that the language models heavily rely on words, not their order, to make decisions. And the root cause was the existence of self-attention matrices that explicitly map word-correspondence between two input sentences regardless of those words’ order. To demonstrate, the researchers used a pre-trained language model, RoBERTa, that achieves a 91.12% accuracy on the Quora Question-Pairs dataset by correctly labeling a pair of Quora questions “duplicate.” And using that model, they show the following effect on shuffled words.
Words not shuffled:
All words in question Q2 shuffled at random. Interestingly, the model’s predictions remain almost unchanged.
The models incorrectly label a real sentence and the shuffled version “duplicate.”
BERT-based language models, which use transformer architectures, that learn representations via a bidirectional encoder are good at exploiting superficial cues like the sentiment of keywords and the word-wise similarity between sequence-pair inputs. They use these hints to make correct decisions when tokens are in random orders. The researchers tested BERT-, RoBERTa-, and ALBERT-based models on 6 GLUE binary classification tasks. The tasks were to classify whether the words in an input sentence were intact. Any human or model that has surpassed humans is expected to choose the “reject” option when asked to classify a sentence whose words are randomly shuffled.
Their experiment showed that 75% to 90% of the accurate predictions of BERT-derived classifiers, trained on many GLUE tasks — five out of six — remain unchanged even after shuffling the input words. It means that 65% of the five GLUE tasks’ ground-truth labels can be predicted when the words in one sentence in each example are shuffled. The behavior persists even in BERT embeddings that are famously contextual.
They also showed that in the sentiment analysis task (SST-2), a single salient word’s ability to predict an entire sentence’s label remains more significant than 60%. Consequently, one can be safely assumed that the models rely heavily on a few keywords to classify a complete sentence.
It was found that models trained on sequence-pair GLUE tasks used a set of self-attention heads to find similar tokens shared between the two inputs. For instance, in ≥ 50% of the correct predictions, QNLI models rely on a set of 15 specific self-attention heads for finding similar words shared between questions and answers regardless of word order.
Modifying the training regime of RoBERTa-based models to be more sensitive to word order improves SQuAD 2.0, most GLUE tasks (except SST-2), and out-of-samples. These findings suggest that existing, high-performing NLU models have a naive understanding of the text and readily misbehave under out-of-distribution inputs. They behave similarly to n-gram models since each word’s contribution to downstream tasks remains intact even after the word’s context is lost. In the end, as far as benchmarking of the language models are concerned, many GLUE tasks now cease to be challenging enough for machines to understand a sentence’s meaning.
Infosys has made a spectacular entrance into the global cloud-as-a-service arena by offering the Cobalt Applied AI Cloud, built on the latest NVIDIA DGX A100s. The company aims to make AI accessible within the workforce to drive AI-driven transformations for enterprises.
NVIDIA DGX™ A100 systems offer the necessary compute power for enterprise-level AI applications. The A100 systems are considered ‘Workstation in a box.’ They host 1 trillion transistors and offer 5Petaflops clock speed upfront. On top of it, NVIDIA’s inbuilt Multi-Instance GPU (MIG) technology further improves infrastructure efficiency and maximizes the utilization of each DGX A100 system. The systems will provide the required hardware and software stacks needed for over 100 project teams to simultaneously run machine learning and deep learning operations.
Charlie Boyle, Vice President and General Manager of DGX Systems at NVIDIA, said, “Many organizations are eager to infuse their business with AI but lack the strategic platform on which they can pool expertise and scale the computing resources needed to build mission-critical AI applications. Working with Infosys, we’re helping organizations everywhere build their own AI centers of excellence, powered by NVIDIA DGX A100 and NVIDIA DGX POD infrastructure to speed the ROI of AI investments.”
The developers and project teams of Infosys can access NVIDIA’s full-stack AI solutions to build services for enterprises. Teams can now develop AI services centrally or locally on any device without any lag. These services will deliver AI-first business processes for enterprises across private and public clouds. Businesses can use these Cobalt AI Cloud services to harness their data and curate data exchanges on the cloud to build and train their AI models. The AI cloud also offers the flexibility to build on top of with services delivered by any hyper-scale cloud provider, to scale and future-proof AI-powered transformation.
Balakrishna D.R., Senior Vice-president of Infosys’ AI & Automation Services, said, “For a long time now, AI has been playing a key role in shaping consumer experience. Cloud, data analytics, and AI are now converging to bring the opportunity for enterprises not just to drive consumer experience but reimagine processes and capabilities too. Infosys applied AI cloud, powered by NVIDIA DGX A100 systems, can help enterprises to quickly build on the opportunity while scaling with new technological advancements.”
Thanks to its recognition in NVIDIA PARTNER NETWORK, Infosys will garner the technical know-how to build NVIDIA DGX A100-powered applications. The premier ones are pre-built ready-to-deploy AI platforms and on-prem AI clouds for enterprises. Other offerings include licensed and open-source AI software-as-a-service (SaaS), models, and edge capabilities.
India’s Ministry of Electronics and Information Technology (MeitY) has recently announced that it will set-up a quantum computing application lab with AWS. With this step, India is trying to catch up with the global players like the US, China, and EU in the High-Risk, High-Reward technology.
Quantum computing uses the quantum mechanical properties of matter as the fundamental unit of computation. It has opened new doors in computing, communications, and cybersecurity with wide-spread applications. Google’s quantum supremacy experiment,Quantum encrypted Satellite communication by China, and more have put the nascent field in the spotlight.
India has made a smart and timely move to give its academia and industries access to cutting-edge Quantum computing technology via AWS Braket service, the cloud-based quantum computing service. AWS Braket seems to be the perfect solution for India’s Quantum woes – low quantum hardware and software capacity. The proposed plan states that a steering committee would be set-up to oversee applications around quantum technologies. Primarily, Call for Proposals (CfPs) will be announced from the research community in February-early March. Selected proposals will get AWS Braket credits with access to quantum computing hardware, simulators, and programming tools.
During the announcement, the secretary of MeitY Ajay Sawhney said, “The area of quantum computing is at a very nascent stage in the country. The Lab is a great opportunity for our researchers, whether in educational institutions, in research labs, or the startup environment. With quantum computing, there is still a tremendous amount that all of us have to learn together. What exactly will it be useful for, how does it get applied to the scenario that we have within the country? These are the questions we must ask.”
The Indian government is aware of the technological shortfall, especially the lack of hardware and software stack to replicate the domestic software industry’s ecosystem and pose as a global academia leader. It had earmarked 8000 crores rupees for the next five years under its National Mission on Quantum Technologies & Applications (NM-QTA) to develop indigenous quantum technologies.
“The Quantum Computing Application Lab in collaboration with AWS is the first lab on AWS that’s aligned to a government mission. We are trying to pull all of the resources needed to build the capacity and the skills within India to propagate this technology in a meaningful way. The company wants to democratize quantum computing and provide the right development environments to remove the heavy-lifting away from researchers and developers working on quantum algorithms. We want to build this in a scalable manner that can be accessible to all,” said Rahul Sharma, the President of Public Sector – AISPL AWS India and South Asia.
The U.S. government recently exempted automated vehicles (AVs) from some crash standards. The main reason is to cut production costs, promote faster deployment, and amplify mass-adoption. Based on an estimated production of 5.8 million vehicles, the National Highway Traffic Safety Administration (NHTSA) estimates, AV manufacturers would save up to $5.8 billion in the year 2050, i.e., $995 per vehicle.
Previously, the test procedures for AVs were similar to conventional vehicles — the same 80+ tests. Now, the relaxed norms include eliminating the driver’s seat of self-driving vehicles. There will be no crashworthiness standards for automated delivery vehicles as they will never have a human occupant inside of them. These vehicles would still be tested for pedestrian safety and safety-assist systems, but they would no longer need airbags, seat belts, and seats.
For shared passenger-carrying AVs, all front-faced seating positions will now be considered “front outboard passenger seats.” These seats will be subject to traditional regulations, including the placement of advanced airbags. AVs will no longer have a defined driver’s seat, but a child could sit in the front left seat. A vehicle that would permit this, there would be ways to deactivate the baby seat’s airbag.
People have lost lives because there lies confusion between what autonomous vehicles and automated vehicles are. The National Transportation Safety Board stated that no self-driving cars (SAE level 3+) were available for consumers to purchase in 2020. NHTSA had adopted SAE AV classification for Automated Driving Systems which consists of 6 levels – Level 0, Level 1 (“hands-on”), Level 2 (“hands-off”), Level 3 (“eyes off”), Level 4 (“mind off”) and Level 5 (“steering wheel optional”). From SAE 3 onwards, the human driver need not monitor the environment, and the automated system takes over. At SAE 3, the human driver still has a responsibility to intervene when asked to do so by the automated system. The human driver is always relieved of that responsibility at SAE 4, and the automated system will never need to ask for intervention in SAE5.
NHTSA runs the Automated Vehicle Transparency and Engagement for Safe Testing (AV-TEST), which provides an online, public-facing forum for sharing automated driving system on-road testing activities. Automated Vehicle-TEST included more participants after the Partners for Automated Vehicle Education (PAutomated VehicleE) poll suggesting that in American majority do not think autonomous cars are “ready for primetime.”
The test remains controversial because NHTSA relies on voluntary industry interactions. At least 80 companies are testing autonomous vehicles and only 20 have submitted safety assessments to the Transport Department under the current voluntary guidelines set for four years. At the same time, the National Transportation Safety Board (NTSB) has investigated six AV-related crashes revealing problems like inadequate countermeasures to ensure driver engagement, reliance on voluntary reporting, lack of standards, low corporate safety culture, and a misguided oversight approach by NHTSA.
The incompleteness of the data also plagues the test because companies like Pony.ai, Baidu, Tesla, Amazon, Postmates, and Motion have declined, in the past, to provide tracking data. The Automated Vehicles 4.0 (AV 4.0) guidelines also do not mandate regular self-driving vehicle safety assessments. It permits automakers to carry out AV assessments themselves rather than by standards bodies. All these facts simply make the automated vehicle business insidious for the common man. At the same time, it also raises questions about the NHTSA’s credibility to measure safety when the tracking data is not available in the first place.
The space scientists at NASA’s Jet Propulsion Lab (JPL) have trained an AI that detects new craters from the image data archives of Mars Reconnaissance Orbiter. And as of now, the team has submitted 20+ craters for additional validation.
NASA had deployed its Mars Reconnaissance Orbiter (MRO) on Mars in 2006. It has been 15 years since then, and the orbiter completed 60,000+ orbits beaming back 404 Terabits of data. The two onboard cameras – HiRise and Context have the lion share of the transferred data in the form of high-resolution images.
A crater is produced by the impact of a meteorite, volcanic activity, or an explosion. They are the subject of interest for cosmologists because these structures provide cosmological cues about the age of the planet Mars and the activities going below its surface. The smallest impact-craters are harder to find because their diameter can be as little as 4 meters and to gauge out one such crater from 316 km above the Martian surface requires substantial technical effort. First, the Context Camera detects them in its low-resolution camera covering hundreds of miles at a time. Next, the high-resolution HiRise Camera is used to validate the finding.
It takes NASA scientists around 40 minutes to manually crawl through one image data to discover new craters. Therefore, JPL researchers trained an AI model that automated fresh impact crater classification to reduce the inference period, from detection to validation. For the model training, the researchers fed 6,830 positive Context Camera images marked with locations of previously discovered impacts confirmed via HiRISE. The training data also had negative examples, images with no fresh impacts. For testing the AI, the researchers used 112,000 Context images. As an immediate result, the whole inference time was reduced from 40mins to an average of five seconds. The NASA AI has detected 20+ craters as of now, and those findings have been submitted for validation via HiRise imaging.
The Bangalore-based cure.fit, a health and fitness company, quietly acquired a US-based fitness startup, Onyx, for an undisclosed amount. Cure.fit aims to integrate Onyx’s body tracking technology to provide personalized coaching in its services like live.fit, care.fit, mind.fit, and more.
Cure.fit has secured over $400 million funding across nine rounds recently and is busy spending the money on capacity building. Onyx is the company’s seventh acquisition after acquiring Rejoov, Fitness First India, Seraniti, Kristys Kitchen, a1000yoga, among others.
The global digital fitness market may reach $59.2 billion by 2027, growing at a CAGR of 33.1 percent. Indian market is also likely to reach $2.15 million in 2021 and grow at a CAGR of 2.7 percent to become worth $2.33 billion by 2024, as per Statista. During this time, Cure.fit is undoubtedly making a statement by acquiring an off-shore startup that points towards their international business aspirations.
The acquisition will also strengthen the cure.fit’s at-home fitness solutions that currently not at par with their live-training sessions offered at their centers. Hence, cure.fit will use Onyx’s AI platform, which provides human-like personalized guidance in various fields, from physical and mental wellness to nutrition. The platform tracks motion without wearables, eliminating the need for visiting gyms/centers. Onyx’s AI platform also gives trainers the ability to coach a larger audience without physically interacting with them.
“The 20s will be the decade of digital health. Onyx will accelerate our efforts towards building a hardware-agnostic AI-led platform that offers guided content on physical and mental wellness and nutrition all at the same place. Users will get a personalized experience with high-quality tech and human touch, and will be able to achieve their fitness goals from the comfort of their homes without spending on expensive hardware,” Mukesh Bansal, co-founder at Cure.fit said.
Cure.fit currently uses its energy-meter tech, where the phone camera tracks users’ movements as they try to follow guided content from trainers. Adding Onyx’s real-time analysis, cure.fit can improve the personalized measurement element of its platform. The platform can also judge users’ performance, correct issues with the form, and offer properly timed motivations, just like a human coach. This will allow the company to expand the range of tracked activities to include dance and yoga and fast-paced activities like high-intensity interval training while accurately gauging a user’s compliance with proper forms and pacing.
Onyx claims it has “the world’s first 3D motion capture system on your phone” and says it’s capable of tracking “nearly any exercise,” from squats to planks, push-ups, kicks, running, and jumping. A user can place a smartphone in a location capable of capturing the full body during exercise; the app then uses computer vision to render the user as a silhouette while automatically comparing limb and torso motions against ideal representations.
“High accuracy body tracking combined with studio-quality content, will help us create a very differentiated experience for our users,” said Asaf Avidan Antonir, Co-founder of Onyx.
