At the NVIDIA GTC 2022, DeepBrain AI’s Chief Technology Officer (CTO), Kyung-Soo Chae, presented under “Toward Real-Time Audiovisual Conversation with Artificial Humans.” DeepBrain AI’s lip-sync video synthesis technology introduced during the event enables real-time video and voice-based communication between AI and humans.
The NVIDIA GTC, a global technology conference held as an online live session from March 21 to 24, was attended by companies worldwide. These companies participated in over 500 live sessions, including data science, high-performance computing, deep learning artificial intelligence, and robotics technology, to showcase their recent technological advancement and discuss related agendas with participants.
This new technology introduced by DeepBrain AI can generate high-resolution lip-sync videos at 4x speed of time through its own artificial neural network structure design. The company also announced research results that reduced synthesis time to 1/3 by applying NVIDIA’s deep learning inference optimization SDK.
DeepBrain AI also participated in a panel discussion session on “Digital Human and Convergent AI” to announce AI human technology and specific business status. Real-time questions and answers followed the session, and the company introduced DeepBrain AI’s technology use cases and future business plans.
DeepBrain AI CTO Kyung-Soo Chae said, “The area that takes the most time and money to implement AI humans is video and voice synthesis. DeepBrain AI’s unique technology has announced reducing high-quality video synthesis time in 1/12 of real-time.”
DeepBrain AI is regarded as one of the top three global companies for interactive AI. It used deep learning-based video synthesis and voice synthesis source technologies. They have attracted attention as an AI Human solution that creates a virtual human capable of real-time two-way communication through deep learning AI technology.
Google announced a significant update to Google Meet yesterday, and it includes a vast number of long-requested features. The main additions are in-meeting reactions, emoji-based feedback, and the ability to use Meet right inside of Docs, Sheets, and Slides. Users also get a new picture-in-picture mode to allow multitasking or more easily ignoring a meeting. With the latest update, users can stream a meeting to YouTube.
Another major highlight of the announcement is security improvements. Google will roll client-side encryption in Meet in May, an update that is currently still in beta. With this launch, users will be better able to control their encryption keys. Google will also roll out end-to-end encryption for meetings later this year.
“We know we need solutions that help people build connections that can bridge the gap between physical spaces and somewhere else,” said Dave Citron, Google’s director of product management for Google Meet and Voice.
Dave noted that most of these updates focus on collaboration equity and ensure that users can contribute to meetings regardless of their experience level, role, location devices, and language.
The company will also launch personal video tiles for every participant, even if they are in a conference room with others. This update will help remote employees to have the same experience as those working in a physical space.
However, these new Google Meet features will roll out later this year, and features such as responding with emojis during a meeting will have to wait until next month. Picture and picture will also roll out next month, whereas automatic noise cancellation will roll out for all users.
Dynamic robots and software developing company Boston Dynamics announces that its new Stretch robot is now available for commercial purchase.
The unique robot is specially designed to perform tasks involved in warehouses and distribution centers. Boston Dynamics claims that the Stretch robot is the world’s most advanced robot in its designated field of operations.
Stretch was first announced in 2021, and the company is now accepting reservations for the same for 2023 and 2024 deliveries.
According to the company, all the units built for 2022 have already been sold, proving this robot’s popularity. Some of the early customers of the Boston Dynamics’ Stretch robot include DHL Supply Chain, Gap, H&M, and Performance Team.
Stretch is a mobile robot that unloads floor-loaded containers and packages with complex geometries. It has a powerful custom vacuum gripper that can handle up to 50 pounds at a time. Stretch is designed to maneuver in and out of trucks and tight locations in a warehouse, with improved mobility and a footprint the size of a pallet.
An added advantage of Stretch is that it requires no pre-programming of SKU numbers or box sizes as the robot makes all the necessary unloading decisions in real-time without the need for additional human support or supervision.
CEO of Boston Dynamics, Robert Playter, said, “Stretch makes logistics operations more efficient and predictable, and it improves safety by taking on one of the most physically demanding jobs in the warehouse.”
He further added that many of the early adopter customers have already committed to deploying the robot at scale, so they are happy that Stretch will soon be put to work in a larger capacity, assisting shops and logistics organizations in dealing with the ongoing surge in demand for goods.
Solomon Hykes left Docker four years ago, and today his next startup, Dagger, is launching into public beta and raises $20M Series A funding round. Dagger’s funding round was led by Redpoint Ventures, with participation from Y Combinator, Nat Friedman, Brian Stevens, Idit Levine, Julius Volz, Ellen Pao, and Daniel Lopez. Previously, Dagger raised a $3 million pre-seed and $7 million seed round led by New Wave.
Dagger was co-founded by Hykes and his fellow Docker alums Sam Alba and Andrea Luzzardi. They aim to build a “DevOps operating system.” The team started by listening to people and finding problems they could solve. DevOps quickly came up to be an interesting issue.
The Dagger team argues that there are various effective DevOps tools, but they tend to be very specialized. However, the problem is that developers have to glue these tools together — glue is the bottleneck. So they are focusing on replacing the glue with something better.
Dagger will use the funding to build developer relations and marketing teams and also use funding to expand its engineering team to build its product.
Specifically, Dagger lets DevOps engineers write their pipelines as declarative models in CUE (configure, unify, execute). This platform does not replace CircleCI or GitLabs. Instead, Dagger is a layer on top of existing CI infrastructure and it streamlines supply chain management between infrastructure and deploying software to the cloud.
It will be a hybrid program with an open-source engine that has been launched today and an optional tightly integrated cloud service. The managed cloud service will come later.
Artificial intelligence company Builder.ai raises $100 million in its recently held funding round led by Insight Partners. Multiple existing and new investors like IFC and Jeffrey Katzenberg’s WndrCo also participated in Builder.ai’s series C funding round.
Builder.ai will use the fresh funds to take steps towards accomplishing its aim of aiding organizations and entrepreneurs in using digital transformation to unleash their creativity and potential.
The company said that it would also utilize the funds to expand its AI and automation capabilities along with accelerating the development of its proprietary low-code/no-code platform.
Co-founder and MD of Insight Partners, Jeff Horing, said, “Builder.ai has spearheaded a new category in the low-code/no-code industry with an innovative business model and clarity of vision, fueling its 300% growth in the last year.”
United Kingdom-based artificial intelligence firm Builder.ai was founded by Sachin Dev Duggal and Saurabh Dhoot in 2016. The company is best known for its AI-powered platform designed to help build and operate software projects.
Without requiring technical experience or any lines of code, the platform and human-assisted AI aid enterprises, small businesses, and entrepreneurs in building, running, and scaling their software.
To date, the company has raised nearly $195 million from multiple investors over three funding rounds. Interested users can visit the official website of Builder.ai to book a free demo of the company’s platform.
Co-founder and Chief Wizard of Builder.ai Sachin Dev Duggal said, “We believe that everyone, every business should be empowered to unlock their human potential, whether it’s creating new ideas or digitally transforming their business, and because of this, our choice of investor for this round was very deliberate.”
He further added that this funding round is for their consumers, for the tinkerers, the folks who never took “no” for an answer, the small firms who said “we shall prevail,” and the entrepreneurs who never blinked.
Black Crow AI raises $25 million in a Series A funding round led by Imaginary Ventures with participation from existing investors such as Bloomberg Beta, Interlock Partners, Primary Venture Partners, and Vast Ventures.
CEO Richard Harris said that Black Crow AI will use the funding now exceeding $30 million to “accelerate the development of new and accessible ML use cases in both digital commerce and adjacent verticals” and expand the team across “product, client service, and commercial.”
Harris also said that companies are generating and preceding the volume of real-time data from internal operations, customers, marketing activities, and suppliers. New York-based AI company was founded in 2020 by Harris and Shehzad Khan alongside entrepreneur Damon Tassone.
The idea behind Black Crow was to create a platform that could deliver eCommerce-relevant predictions via an API that integrates with existing tools, workflows, and software. Black Crow runs on top of retailers’ websites and uses streaming event data in customers’ browsers to train AI models and generate predictions while the users are still on the site.
The idea behind Black Crow was to create a platform that could deliver eCommerce-relevant predictions via an API that integrates with existing tools, workflows, and software. Black Crow runs on top of retailers’ websites and uses streaming event data in customers’ browsers to train AI models and generate predictions while the users are still on the site.
Global semiconductor manufacturing giant Intel announces its plans to acquire workload optimization startup Granulate.
However, no information has been provided by either of the companies regarding the terms and valuation of the acquisition deal. The complete transaction is expected to close by the second quarter of this year.
Granulate’s acquisition will enable Intel’s cloud and data center clients to improve compute workload performance while lowering infrastructure and cloud costs.
Intel is taking this step to expand its operations in Israel and the capabilities it offers customers to manage traffic on Intel-powered equipment.
According to the deal, 120 Granulate employees will be integrated into Intel’s Datacenter and AI business unit.
Modern designs of cloud computing have generated more complicated performance concerns that traditional operating systems and runtimes are ill-equipped to handle. This addition to Intel’s team will considerably help the company tackle this challenge.
Intel says that it is dedicated to assisting clients in structuring their compute clusters, instance types, and cloud deployments correctly.
Executive Vice President and General Manager of the Datacenter and AI Group at Intel, Sandra Rivera, said, “Today’s cloud and data center customers demand scalable, high-performance software to make the most of their hardware deployments.”
She further added that Granulate’s autonomous optimization software could be applied to production workloads without needing a change in the customer’s code, resulting in optimal hardware and software value for every cloud and data center customer.
Israel-based AI-powered optimization software developing company Granulate was founded by Asaf Ezra and Tal Saiag in 2018. The startup specializes in providing software that drastically improves performance by creating a streamlined environment for any application.
Granulate’s autonomous optimization service reduces CPU utilization and application latencies. To date, Granulate has raised more than $45 million from investors like Red Dot Capital Partners, Insight Partners, and several others over four funding rounds.
“As a part of Intel, Granulate will be able to deliver autonomous optimization capabilities to even more customers globally and rapidly expand its offering with the help of Intel’s 19,000 software engineers,” said the Co-founder and CEO of Granulate, Asaf Ezra.
In the most startling chain of events, hackers have stolen cryptocurrencies over worth $600 million from an online game, in what is believed to be the largest crypto heist ever. Sky Mavis stated that the Ronin Network, which hosts their Axie Infinity game, was hacked, with hackers taking a total of $620 million in 173,000 Ether and $25.5 million in USDC. This was accomplished by gaining unauthorized access to the Ronin Bridge, which connects Ronin’s blockchain to other cryptocurrencies. Bridges are tools that facilitate the production of synthetic derivatives that replicate assets from a different blockchain.
The issue wasn’t identified until Tuesday, when an Axie Infinity user attempted to withdraw 5,000 ETH worth of money from the game but was unable to do so, sparking an investigation. This attack outperforms the $611 million hack of the Poly Network, a decentralized finance platform, in August 2021.
According to the official report, the attacker was able to sign transactions from five of the Ronin network’s nine existing validator nodes, which is the required level for signature approval. The attacker eventually acquired access to Sky Mavis’ four validators as well as one run by Axie DAO. The validator key method is set up to be decentralized to prevent an attack vector like this hack, however, the attacker discovered a backdoor through the gas-free RPC node, which they exploited to get the signature for the Axie DAO validator.
Validator nodes are a characteristic of proof-of-stake blockchains including Ronin, which use less energy than proof-of-work systems like Bitcoin and Ethereum. New transactions are reviewed by the nodes to ensure that their inputs and outputs match and those authorization signatures are genuine, and any transactions that do not comply are denied. Although employing fewer nodes is quicker and more efficient, as the breach demonstrates, if a majority of the nodes are hacked, security issues arise, especially if they are not audited. It’s a possible flaw for blockchains marketed as being less expensive and more environmentally friendly than Ethereum. For instance, Binance Smart Chain, one of the world’s fastest-growing networks, relies on just 21 validators, rendering it vulnerable to external attacks, much like Ronin.
To put things in perspective, Ethereum presently has 222,052 validators working together to protect over 7 million ETH. This means that in order for any verification, voting, or record-keeping procedure to be accepted, a majority of these validators must agree.
The company has stated that it is trying to increase the validator threshold from five to eight in order to minimize future hacks. It was also disclosed that the team was already in contact with major cryptocurrency exchanges and with Chainalysis, in order to notify them when funds are transferred to either of them. The Ronin Bridge has been momentarily suspended at the same time. Binance has also deactivated its Ronin-to-Binance bridge to be on the safe side. The bridge will be unlocked when the company is convinced that no more cash may be drained. Due to the difficulty of arbitrage and transferring additional coins to Ronin Network, Sky Mavis has also temporarily blocked Katana DEX. Meanwhile, members of the crypto community are responding to the news of the breach, with some questioning how the hack went unnoticed for over a week. In addition, the hack prompted a 23% decline in the price of Ron, the token featured in Ronin’s blockchain, as stated by CoinMarketCap. Even AXS, a token used in Axie Infinity, fell 6%.
Axie Infinity co-founder Aleksandr Leonard Larsen promised to compensate consumers. According to him, the theft was facilitated by “a social engineering attack paired with a company error dating back to December 2021.” According to Sky Mavis, the company resorted to using a shortcut in November of last year to relieve an “immense user load” on its network, months after the game skyrocketed in popularity in the Philippines and other nations where players used it as a full-time job. The system was shut down in December, but the whitelist permissions that made it possible were never revoked.
Social engineering is a cyber security phrase that refers to deceiving customer care representatives into giving someone access to their online account.
Mr. Larsen stated that the company is committed to recovering or reimbursing all of the money that has been drained while also consulting with its stakeholders to determine the best course of action.
Axie Infinity is a play-to-earn game in which gamers would mint and collect NFT-based creatures that are similar to animated monsters in the Pokémon universe. Breeding, battling, and expanding their army with these creatures known as Axies can earn them in-game tokens. DappRadar, a blockchain sales tracking company, said in October 2021 that over 615,000 traders had bought or sold Axie Infinity NFTs in 4.88 million transactions, with an average sale price of $420. It surpassed the $4 billion milestone in lifetime NFT sales in February.
Last year, Sky Mavis, collected $152 million from investors like a16z, FTX bitcoin exchange, and Samsung Next, increasing its worth to $3 billion.
Typically, Ethereum is used for the majority of the game’s transactions. However, due to the high costs associated with ETH, doing multiple transactions each day is highly expensive. This made Axie Infinity’s developers unveil Layer 2 solution Ronin, an Ethereum-based chain that permitted 100 free transactions each day, in February 2021. Transactions on that network can be completed far faster, for less money, and with less environmental effect than transactions on Ethereum. This resulted in massive growth, with the game’s community reaching 2.9 million members by the year-end.
While Sky Mavis was setting up a network of computer nodes to authenticate transactions on its Ronin Network, it saw that if hackers could take 51 percent control of the network, they could make fraudulent transactions and steal assets.
PeckShield, a cybersecurity firm specializing in blockchain technology, has released a flowchart illustrating where the funds were transferred. The hacker moved cryptocurrency stolen from Ronin Bridge to a number of unidentified cryptographic addresses.
Diagram showing the transfer of stolen funds. Source: Twitter / @ peckshield
While the attacker’s primary wallet “0x098B716B8Aaf21512996dC57EB0615e2383E2f96” still holds the majority of the crypto assets, they transmitted 1,220 ETH to FTX, 1 ETH to Crypto.com, and 3,750 ETH to Huobi. The hacker was converting USDC 25.5 million into ETH too. They began moving funds to several addresses on March 28 of this year. Huobi and Binance, two major trading exchanges, have reported that they will help Axie Infinity by looking out for any suspicious asset transactions.
Huobi will fully support @AxieInfinity as it deals with the aftermath of the attack and theft on its Ronin chain. Any stolen crypto assets that have been discovered to have traversed our exchange and related networks will be dealt with expediently.
The Ronin hack comes after a February attack on the Wormhole bridge, which resulted in more than $300 million in damages that were paid by Jump Crypto, one of Wormhole’s sponsors.
Artificial intelligence (AI) is frequently viewed as a game-changer for the military, with governments all over the world investing heavily in AI to upgrade their forces. So far AI technologies have been used for mass surveillance and target locking activities in the military world. It is expected that major advances in artificial intelligence will be made in the coming years, paving the way for robust, distributed command and control, and removing the human operator from mission command. When this happens experts believe AI will usher in a new age of autonomous decision-making in the military. While AI-powered autonomous systems are already widely used in areas like healthcare, transportation, and digital services, real military autonomy is still a work in progress. However, with Defense Advanced Research Projects Agency (DARPA) planning to introduce an AI-based decision-making program for medical triage, things may take a new course.
This new DARPA AI endeavor, dubbed ‘In the Moment,’ (ITM) will leverage AI technologies that can make critical choices during tense situations based on real-time data analysis, such as patients ’ conditions in a mass-casualty event and drug availability. This is revolutionary because, in a real-life emergency situation where instantaneous decisions must be made about who gets immediate medical assistance and who doesn’t, the answer isn’t always apparent and people tend to disagree on the best course of action, AI will make a quick assessment. As a result, the United States military is increasingly relying on technology to minimize human error, with DARPA claiming that eliminating human bias from decision-making will ‘save lives.’
In the Moment is different from traditional AI development approaches, according to Matt Turek, In the Moment program manager, since it does not require human consensus on the proper outputs. Turek adds that in complex cases, the lack of a correct response inhibits the team from employing traditional AI assessment procedures, which require human agreement to establish ground-truth data. Self-driving car algorithms, for instance, can be based on ground truth for correct and incorrect driving responses, such as traffic signs and road restrictions.
Turek says,” When the rules don’t change, hardcoded risk values can be used to train the AI, but this won’t work for the Department of Defense (DoD).
Therefore, the In the Moment software will be entrusted with collaborating with trusted human decision-makers to determine the best course of action to follow when there is no evident agreed-upon appropriate solution. For example, AI might assist in identifying all of the resources available at a nearby hospital, such as drug availability, blood supply, and medical staff availability, to aid in decision-making. The whole concept is loosely inspired by the medical imaging analysis field. ‘Building on the medical imaging insight, ITM will develop a quantitative framework to evaluate decision-making by algorithms in very difficult domains,’ Turek added.
There are four technical areas in the program. The first is to create decision-maker characterization techniques that identify and quantify key decision-maker characteristics in challenging domains. The second area is developing a quantitative alignment score between a human decision-maker and an algorithm that reflects end-user trust. Designing and implementing the program evaluation is the responsibility of the third technical area. The program’s last technical area is in charge of policy and practice integration, as well as offering legal, moral, and ethical knowledge.
According to DARPA, the new AI would take two years to train and another 18 months to prepare before being employed in a real-world scenario. Though the experiment is still in its early stages, it comes at a time when other countries are attempting to revamp a centuries-old medical triage system, and the US military is increasingly relying on technology to reduce human errors in conflict. However, scientists and ethicists are skeptical about the proposal, questioning if AI should be engaged when human life is on the line.
The In the Moment program will develop and test algorithms to help military decision-makers in two different scenarios: small unit injuries, such as those experienced by Special Operations forces under fire and large casualty events, such as the bombing of Kabul’s airport. According to agency officials, they may later design algorithms to help disaster relief crises like earthquakes.
Various individual and algorithmic decision-makers will be provided scenarios from the medical triage or mass casualty domains to evaluate the whole In the Moment process. Algorithmic decision-makers will comprise an aligned algorithmic decision-maker who understands important human decision-making qualities and a basic algorithmic decision-maker who does not. In addition, as an experimental control, a human triage professional will be added.
Turek adds that the DARPA team will gather the decisions and replies from each of the decision-makers, then submit them to various triage specialists in an anonymized format. These triage specialists will have no way of knowing if the response is generated by an aligned algorithm, a baseline algorithm, or a person. Triage professionals will then be asked which decision-maker they would delegate to, giving the study team an indication of their readiness to trust that particular decision.
DARPA urges anybody applying to join In the Moment AI program to adopt an open-source IP model with unlimited rights, and states that anyone who does not give unlimited rights must make a compelling argument for doing so.
Sohrab Dalal, a colonel and the chief of NATO’s Supreme Allied Command Transformation’s medical department, said the triage protocol, in which medics go to each soldier and determine how urgent their care requirements are, is approximately 200 years old and could use a makeover.
His team is collaborating with Johns Hopkins University to develop a digital triage assistant that NATO member countries can employ, similar to DARPA.
NATO’s triage assistant will combine NATO injury data sets, casualty scoring systems, predictive modeling, and patient condition inputs to produce a model that will determine who should receive care first in a scenario when resources are limited.
Meanwhile irrespective of which organization is working on building an AI-powered medical triage system, not many are on board with the concept. While In the Moment promises to remove human bias, will the training dataset cause the model to prioritize medical attention to a certain class of people over others (e.g. race, rank, and gender bias)? Would military officials still opt for following algorithm-based outcomes, even if their common sense and conscience suggested otherwise? In an event of death who will share the blame? Is it morally and ethically right to hand over the triage decision-making entirely to the AI model or will a hybrid model offer better results on similar grounds? Is relying totally on AI on matters concerning medical assistance a huge risk? In situations where human officers prefer to provide medical aid to those who are least injured so that they can get back to fighting, will algorithms make such exceptions? Can troops trust and accept an AI program calling shots on who gets medical attention first? While computers are capable of making decisions in a fraction of a second, what if the decision made is ‘wrong’ or unacceptable? We are already hearing news of self-driving vehicles causing harm to people in its attempt to navigate on roads by itself, so can we trust AI to not mirror such mistakes during medical triage? Does DARPA have enough info about each situation (present and historical)?
True, AI does not bear any resemblance to what you see in sci-fi movies right now. AI’s neural networks include human decision-making processes and eliminate inconsistencies and biases. Every now and then they have outperformed experts in almost every discipline after being adequately trained. They generally need human involvement and final approval. But, it will bring a huge relief, if DARPA or NATO responds to the concerns stated.
The National Highway Traffic Safety Administration (NHTSA) of the US, says that it plans to review the actions of robotaxi startup Pony.ai regarding crash reporting norms set up by the government.
The investigation would see if the company adhered to federal reporting standards for self-driving car accidents.
According to NHTSA, it plans to check whether Pony.ai compiled with the requirements “with respect to both the timeliness and accuracy of its reports,” reported Reuters.
Following an October incident in California, Pony.ai decided to issue a recall for some autonomous driving system software versions earlier this month. Authorities claim this episode to be the first-ever recall of an automated driving system.
The incident involved a Pony.ai autonomous vehicle that collided with a street sign on a median in Fremont, California, on October 28th, 2021. However, no one was injured in the incident.
After the accident, designated authorities suspended the company’s autonomous vehicle testing permit in the region. According to the company, the accident occurred in less than 2.5 seconds after the automated driving system shut down.
Pony.ai, in its defense, said that it reported the incident to the National Highway Traffic Safety Administration in “a good faith effort to comply with the relevant requirements” and said it “has been fully working with NHTSA throughout the process.” Pony.ai said they have looked into the matter and repaired the three faulty vehicles.
Recently, Pony.ai also unveiled its 6th generation autonomous driving system, including cutting-edge sensors, NVIDIA DRIVE computer platform solutions, and styling and design characteristics for L4 automotive-grade mass production fleets.
