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.
In 2019, Waymo launched one of the largest and most diverse autonomous driving datasets for research, and it consisted of multimodal sensor data from 1,000 driving segments. On March 9, 2022, Waymo introduced additional labels to expand it’s Open Dataset. This expansion includeslabels for key points, pose estimation, 3D segmentation, and 2D-to-3D Bounding box correspondence.
The key point and pose estimation label is a valuable addition to behavior prediction models since it can capture the smallest nuances like detecting a cyclist’s turn gesture. The 3D segmentation label is a great edition for public data sets since it can be used to detect each pixel of an image instead of bounding boxes to represent and classify objects.
Waymo has added 3D segmentation labels for 23 classes of 1,150 segments in the Open Dataset. The 2D-to-3D bounding box correspondence will help researchers to easily associate 2D camera bounding boxes with the corresponding 3D boxes in lidar labels.
The Waymo Open Dataset has two datasets: the Perception dataset with high-resolution sensor data and labels for 1,950 scenes. Since its launch, Waymo has improved the dataset by almost doubling the size of the Perception dataset. The company also introduced a Motion dataset, enabling prediction tasks and it has object trajectories and corresponding 3D maps for 103,354 locations.
Waymo has also announced 2022 Waymo Open Dataset Challenges featuring Motion Prediction, 3D Semantic Segmentation, 3D Camera-only Detection, and Occupancy and Flow Prediction. Each Waymo Open Dataset Challenges winner will receive a $15,000 cash award, with the second-place winner receiving $5,000 and the third-place winner receiving $2,000.
Waymo Open Dataset is one of the most comprehensive, complex, and resource-intensive autonomous driving datasets that has contributed to 500+ publications and provides high-quality data to the research and academic community.
CEO of Tesla and the world’s richest man Elon Musk recently hinted that he might soon start his own open-source social media platform that will support the integration and use of Dogecoin.
Given that Twitter serves as the de facto public town square, failing to adhere to free speech principles fundamentally undermines democracy.
In a recent tweet, Musk said that he was unsure about the policies of Twitter and doubted the platform’s norms in promoting freedom of speech.
He asked his 79 million Twitter followers if they thought Twitter “rigorously conforms” to the notion that “free expression is important to a functioning democracy.” As always, Twitteratis flooded the platform with their opinions following Musk’s tweet.
Free speech is essential to a functioning democracy.
Do you believe Twitter rigorously adheres to this principle?
People showed diverse views regarding the matter, where some supported Musk while others claimed that Twitter was doing just fine. A user tweeted, “Twitter is a PRIVATE company, free speech doesn’t apply to you here.” Some users also urged Elon Musk to buy Twitter.
More than 70 percent of the 2 million respondents clicked ‘no,’ prompting Musk to propose a new platform. Any new platform he creates would include a digital tip jar that would support the meme-inspired cryptocurrency Dogecoin, added the CEO of Tesla.
Musk has always been a supporter of decentralized cryptocurrencies and has earlier also accepted payments via Bitcoin for Tesla, but the service was terminated. A few months earlier, Tesla announced that it plans to accept Dogecoin as payment at its Supercharging station in Santa Monica, United States.
Dogecoin was created by Billy Markus and Jackson Palmer to mock Bitcoin. However, Dogecoin achieved much recognition after it grabbed the attention of Elon Musk. The cryptocurrency’s logo and the name are based on a viral meme involving a Shiba Inu dog.
Massachusetts Institute of Technology (MIT) announces the launch of its new AI Hardware program aimed to boost artificial intelligence innovations in the hardware industry.
It is a new collaboration between academics and industry aimed at defining and building translational hardware solutions for the AI and quantum age.
According to MIT, its newly launched AI Hardware program will develop a roadmap for cutting-edge AI hardware.
MIT School of Engineering and MIT Schwarzman College of Computing have partnered to innovate technologies to deliver enhanced energy efficiency systems for cloud and edge computing.
The MIT AI Hardware Program brings together MIT and industry researchers to help bridge the gap between fundamental knowledge and real-world technical solutions in fields like devices, algorithms, and many others. Jess del Alamo and Aude Oliva are co-directors of the new program, which is chaired by Anantha Chandrakasan.
Dean of MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science, Anantha Chandrakasan, said, “A sharp focus on AI hardware manufacturing, research, and design is critical to meet the demands of the world’s evolving devices, architectures, and systems.”
Chandrakasan further added that the future of high-performance computing depends on collaboration between industry and academics. The program invites multiple industry-leading companies as its inaugural members, including Amazon, Analog Devices, ASML, NTT Research, and TSMC.
MIT says that the AI Hardware program will prioritize various topics like analog neural networks, heterogeneous integration for AI systems, software-hardware co-design, AI edge security, wireless technologies, hybrid-cloud computing, etc.
“We are all in awe at the seemingly superhuman capabilities of today’s AI systems. But this comes at a rapidly increasing and unsustainable energy cost,” said Jess del Alamo.
Jess also mentioned that Continued advancement in artificial intelligence would require the development of new, more energy-efficient systems, which will demand breakthroughs throughout the entire abstraction stack, from materials and devices to systems and software.
Apple recently announced a Supplier Employee Development Fund worth $50 million to expand learning opportunities for upskilling its supply chain.
According to Apple, the funds will be utilized in multiple supply chains, including India, China, Vietnam, and the United States.
Apple will also use the announced funds for new and extended relationships with prominent human rights organizations, universities, and nonprofits to support Apple’s continued efforts to empower suppliers and increase workplace rights awareness and respect across industries.
Over the years, Apple has been providing upskilling programs that allow individuals to gain critical technical and leadership skills required in the industry. The Supplier Employee Development program is a step further towards this goal of Apple by offering new educational resources for people in its supply chain.
Senior Director of Environment and Supply Chain Innovation, Sarah Chandler, said, “We put people first in everything that we do, and we’re proud to announce a new commitment to accelerate our progress and provide even more opportunities for people across our supply chain.”
She further added that they are continuing to drive innovations to benefit people and the earth in collaboration with rights advocates and education experts.
Apart from this program, Apple also announced its 16th edition of the People and Environment in Our Supply Chain report, which describes the ways Apple and its suppliers support the supply chain. The report includes various factors such as the company’s consumption and adoption of clean energy, investment in new-age technologies, and many others.
Moreover, Apple stated that it would engage with UN agencies such as the IOM (International Organization for Migration) and ILO (International Labor Organization) to expand its work, including launching additional programs, training, and worker feedback systems to maintain a safe and respected work environment for employees throughout its supply chain.
Deputy General Manager for Management and Reforms at IOM, Amy Pope, said, “The IOM and Apple partnership has proven results in Apple’s own supply chain and paves the way for others in the industry to follow.” Pope added that Apple’s new promises would assist workers all over the world in a practical and meaningful way.
Online reselling platform Meesho appoints Debdoot Mukherjee as its new Chief Data Scientist to boost the use of data in its platform.
Data science and artificial intelligence have gained immense popularity over the last few years due to their value to any business, especially for companies that operate in the digital space.
Meesho plans to use the expertise of Mukherjee to expand its capabilities in reaching and retaining more customers on its platform.
Mukherjee, a double gold medalist from the Indian Institute of Technology (IIT) Delhi, will be in charge of the company’s attempts to apply artificial intelligence and head a 30-man team to boost the effective utilization of data and AI.
He is an industry expert in the field of artificial intelligence and data science and has previously led teams in multiple leading organizations, including ShareChat, Hike, and Myntra, successfully.
Mukherjee’s past achievements include developing a feed ranking system and AI models for Moj (ShareChat) that were able to drastically increase engagement and user retention.
“I join Meesho feeling deeply connected with their mission of democratizing internet commerce for everyone in India. AI can play a central role in creating an efficient and healthy marketplace, which is trusted and loved by the customers and also ensures fairness and growth to all the sellers,” said Mukherjee.
He further added that he believes that his previous experiences will aid in the development of a world-class AI team and equipment to help Meesho achieve its ambitious goal of creating a single shopping destination for India’s next billion people.
Meesho has already been using AI and ML to offer customized recommendations to its customers and optimize sellers on its platform. This new addition to its team will further help the company unleash artificial intelligence’s full potential for skyrocketing its growth and presence in the highly competitive Indian market.
CTO and Founder of Meesho, Sanjeev Barnwal, said, “I am delighted to welcome Debdoot as the Head of Meesho’s highly energetic AI team. His leadership and deep understanding of the field will help us meet the growing demands of our users, drive innovation and accelerate our sellers’ success.”
