Global technology and services company Bosch announces that it has acquired high-definition maps providing firm Atlatec.
The new acquisition will allow Bosch to considerably increase its autonomous vehicle offerings to its customers. Both the companies have signed an agreement to complete this deal.
However, no information has been provided by the companies regarding the valuation of this acquisition. Atlatec will join the Bosch Cross-Domain Computing Solutions division as an independent company.
Digital maps play a critical part in the development of self-driving technology from the outset, and the higher the level of automation, the more tightly map production and driving plan programming must be integrated.
Bosch says that high-resolution digital maps are vital in making automated driving functions safe and easy to use. Therefore Bosch has acquired Atlatec as it provides all of the necessary solutions for data recording, processing, map generation, and quality control.
With its own sensor box and associated software, Atlatec has created a scalable solution. This new deal will allow Bosch to reach its goal of developing level 4 autonomous driving vehicles.
President of the Cross-Domain Computing Solutions division of Bosch, Dr. Mathias Pillin, said, “The planned acquisition of Atlatec further expands our expertise in the field of high-resolution digital maps and makes us even more diversified. It makes Bosch the only company that can offer its customers all the necessary building blocks of automated driving.”
Digital maps, in addition to onboard sensors with radar, video, and ultrasonic technology, are essential sensors in automated driving. The company’s solution collects raw data and analyzes it using artificial intelligence algorithms to generate accurate maps. An autonomously driving car, for example, can adjust its speed in advance of a tight bend based on the map information.
The Gujarat Forest Department announces its plans to use an artificial intelligence-powered solution to identify Asiatic lions in the region.
The department is using a unique AI software named SIMBA (Software with Intelligence Marking Based Identification of Asiatic lions) to expand its capabilities of spotting lions based on their physical features.
According to officials, the tool will be used to better analyze population demographics and to expand conservation and management efforts to help Asian lions survive. Individual identification helps to improve conservation and management operations along with ecological elements of the species.
Teliolabs, a Hyderabad-based business that develops enterprise software solutions, has built the AI-powered software SIMBA.
The software can also be used to pinpoint a single lion based on gender, name, microchip number, life status, and lactating status information available in the database.
Deputy Conservator of Forest, Sasan, Mohan Ram, said, “The SIMBA works with a deep machine learning technique that matches a point pattern for pair-wise, and that automates the individual identification, based on the variability in the individual’s whisker spot patterns, scars on the face, notches on the ears, and other metadata of the photograph.”
The software can isolate the region of interest (ROI) and segregate them for identification after being trained with enough training data. In addition, SIMBA can be used to create a database with a unique identification number or name. The software is capable of extracting a photograph’s originality and aggregating comparable patterns or marks within the machine learning’s embedding space.
An advantage of SIMBA is that it comes with a very user-friendly interface, making the software easy to operate. Experts believe that SIMBA can play a significant role in boosting the government’s efforts to conserve the species in the region.
The Scientists of Max Planck Institute for Intelligent Systems developed “Insight,” a robust and soft haptic sensor, which leverages the capabilities of computer vision and deep neural networks to estimate from where and how it is being touched or contacted. The sensor also determines the strength and magnitude of the applied forces.
The research work by MPI-IS is regarded as a massive step for making robots feel and sense their surroundings as precisely as people and animals. Imitating its natural predecessor, the haptic sensor is made to be extremely sensitive and durable.
By sensing the contact and friction of external objects, the sensor can easily identify the touch. The sensor is made of a soft shell that surrounds a lightweight stiff skeleton, which makes the respective sensor look like a thumb. The stiff skeleton supports the sensor structure in the same way that bones support the tissues of the finger.
To provide sensitivity, robustness, and soft contact, the sensor shell consists of a single sheet of elastomer over-molded over a strong frame. Furthermore, the elastomer is combined with dark yet reflective metal flakes to create an opaque grayish finish that keeps any outside light out. A small 160-degree fish-eye camera placed within this finger-sized cap helps in recording beautiful images lighted by a ring of LEDs.
The sensor also has a nail-shaped zone that is comparatively thinner than the other parts, making the sensor more accurate in sensing external contacts. The scientists made an elastomer with a thickness of 1.2 mm for this super-sensitive zone rather than the 4 mm, which is utilized on the remainder of the finger sensor. In addition, the haptic fovea is built to sense even the smallest forces and intricate object forms.
The respective sensor works by sensing and predicting the external contact or touch. When any object comes in contact with the sensor’s shell (outer layer), the color pattern inside the sensor changes. The sensor’s camera continuously captures the pictures at a high rate and feeds the image data to a deep neural network. Even the tiniest change of light in each pixel is detected by the algorithm. The well-trained machine learning algorithm can map out precisely where the finger is striking or touching the shell and calculate the strength and magnitude of the applied force. The model also generates a force map, which gives a force vector for each point in the three-dimensional fingertip.
The Indian MOS&T (Ministry of Science and Technology) has recently announced the plan of introducing nine more supercomputers at well-known institutions across the nation. As per the ministry, the nine supercomputers will be installed at Indian institutions such as IIT Bombay, IIT Madras, IIT Delhi, IUAC Delhi, IIT Patna, CDAC-Pune, SNBNCBS, NCRA Pune, and NIC Delhi.
The supercomputers have been installed and commissioned under the scheme of the National Supercomputing Mission (NSM). According to the MOS&T, the National Supercomputing Mission was established to strengthen the country’s research skills and capabilities by linking them to form a supercomputing grid, also with National Knowledge Network (NKN) serving as the backbone.
Previously, several indigenous supercomputers were launched at Indian academic institutes as part of the National Supercomputing Mission. PARAM Shivay was the first supercomputer built in India, which was installed at IIT (BHU). After the successful installation of Param Shivay, several supercomputers like PARAM Shakti, PARAM Brahma, PARAM Yukti, and PARAM Sanganak were installed at IIT Kharagpur, IISER Pune, JNCASR, and IIT Kanpur, IIT Hyderabad, NABI Mohali, and CDAC Bengaluru, respectively.
In addition, in the first week of February, a supercomputer named Param Pravega was installed at the Indian Institute of Science (IISc) Bangalore under the NSM mission. Param Pravega is said to be one of the most powerful supercomputers of the country, as well as the largest supercomputer deployed in all Indian academic institutions.
As a part of the NSM scheme, the mission has also trained over 11,000 HPC-aware personnel and faculty, resulting in the next generation of supercomputer specialists. Furthermore, four NSM nodal centers HPC (High Performance Computing) and AI training have been created at IIT Kharagpur, IIT Madras, IIT Goa, and IIT Palakkad to extend the activities of HPC training.
According to the MOS&T’s press release, “Powered by the NSM, India’s network of research institutions, in collaboration with the industry, is scaling up the technology and manufacturing capability to make more and more parts in India, taking indigenous manufacturing to 85%.”
After a big rebranding from Facebook, Meta hosted its inaugural conference to unveil its latest Metaverse projects. The virtual Meta event ‘Inside the lab: Building for the metaverse with AI’ was hosted on February 23 at 10:30 p.m. (IST) and featured speakers from across the AI spectrum, with CEO Mark Zuckerberg delivering the opening and closing addresses.
The Metaverse is now a headlining topic, particularly in terms of its potential to revolutionize how we work, live, and play. The premise that the Metaverse will be a well-defined ideal of a technological future that will accomplish for society what the Internet did for the twenty-first century has already been pursued by tech pundits.
Even Zuckerberg is betting that the Metaverse will be the successor to the mobile Internet. While highlighting the difficulties in creating the Metaverse, Zuckerburg stated that AI is the key to unlocking many breakthroughs. Furthermore, because these worlds will be dynamic and constantly changing, AI needs to grasp the context and learn the same way humans do. Zuckerberg said the Metaverse would consist of immersive worlds the user can create and interact with, including the position in 3D space, body language, facial gestures, etc. “So, you experience it and move through it as if you are really there,” he added.
No Language Left Behind
At present we have numerous tools and apps that help us communicate in common languages like English, Spanish, and Mandarin with online tools and software. However, there are billions of people who are not able to access the Internet and its offerings in their native languages. While machine translation systems are catching up, the key problem is building such tools when there are fewer or almost no textual resources.
According to Zuckerberg, Meta AI is invested in developing language and machine translation technologies that will cover the majority of the world’s languages. And now, two more projects have been added to the mix. The first is No Language Left Behind, a new advanced AI model that can learn from languages with fewer samples to train from, which will enable expert-quality translations in hundreds of languages, from Asturian to Luganda to Urdu. This will be a huge milestone in NLP-based AI tools that focus on eliminating the language barrier in accessing the Internet.
Universal Speech Translation
He also stated that Meta’s research division is working on a universal speech translation system that will let users engage with AI more efficiently within the company’s digital realm. According to Zuckerberg, the main objective is to create a universal model that includes knowledge from all modalities and data collected by rich sensors. Zuckerberg said, “This will enable a vast scale of predictions, decisions, and generation as well as whole new architectures training methods and algorithms that can learn from a vast and diverse range of different inputs.”
Project CAIRaoke
Meta is focusing on AI research to allow users to have more natural interactions with voice assistants, revealed Zuckerberg, shedding light on how humans will connect with AI in the Metaverse. During the Meta AI: Inside the Lab event, meta introduced Project CAIRaoke, a new approach to conversational AI for chatting with chatbots and helpers. The project aims to use neural models to help chatbots better comprehend people when they talk in a conversational manner, enabling more fluid interaction between people and their gadgets using natural language processing.
People will be able to communicate naturally with their conversational assistants using models created with Project CAIRaoke, allowing them to go back to anything from previously in the discussion, shift topics entirely, or say things that require complicated, nuanced context. They’ll also be able to communicate with them in new ways, such as through gestures.
Traditional AI assistants rely on four distinct components: natural language understanding (NLU), dialog state tracking (DST), dialog policy (DP) management, and natural language generation (NLG). These disparate AI systems must then be integrated for hassle-free conversation, making them challenging to optimize, slow to adapt to new or unfamiliar jobs, and reliant on time-consuming annotated data sets. In addition, changes in one component might also break the others, requiring all following modules to be retrained, slowing them down. On the other hand, building a model using Project CAIRaoke will eliminate the reliance on upstream modules, thereby speeding up development and training, and allowing users to fine-tune other models with less time and data.
A demonstration of Project CAIRaoke technology showed a family using it to help make a stew, with the voice assistant warning that salt had already been added to the pot. The assistant also observed that they were low on salt and placed an order for more.
Meta also wants to incorporate Project CAIRaoke into devices with AR and VR, and it will be paired with Meta’s video-calling Portal device. Jérôme Pesenti, Meta’s VP of AI, said the company is currently limiting the answers of its new CAIRaoke-based assistant until it is assured that the assistant didn’t create foul words.
BuilderBot
At the Inside the Lab event, Meta also revealed it is also developing a virtual world AI called BuilderBot, which is based on the same conversational technology and allows individuals to talk their own worlds into existence literally. When BuilderBot is turned on, a user may walk into an empty 3D area, which is only populated with a horizon-spanning grid, and tell the bot what they want to appear in the world.
During the demonstration, Zuckerberg started engaging with a low-resolution virtual environment and began populating it with voice commands. With only verbal commands, Zuckerberg’s avatar transformed the entire 3D landscape into a park and eventually a beach. The BuilderBot then generates a picnic table, boom box, beverages, and other minor items in response to spoken commands.
However, it’s unclear if Builder Bot uses a library of predefined items to fulfill these tasks or if the AI creates them from scratch. If the latter is true, it will open new avenues for generative AI models.
Next, to showcase Meta’s commitment to AI transparency and open science, Meta unveiled TorchRec, a library for creating SOTA recommendation systems for the open-source PyTorch machine learning framework, at the Inside the Lab event. It’s available as PyTorch memory and includes primitives for common sparsity and parallelism, allowing researchers to create the same cutting-edge personalization that Facebook Newsfeed and Instagram Reels employ.
AI Learning Alliance
Meta also stated that it would extend free education initiatives focused on attracting more racial minorities to the field of technology, which experts believe is necessary to develop AI systems that are devoid of bias. Through its online learning platform Blueprint, Meta’s AI Learning Alliance aims to make machine learning curriculum available to everybody. The company is also building a consortium of teachers who will teach this curriculum at colleges with large populations of students from underrepresented groups.
No code artificial intelligence-powered applications and services providing company Sway AI partners with hybrid multi-cloud network deploying firm Trilogy Networks to promote AI technology for precision farming in the United States.
Together the companies will join the Rural Cloud Initiative (RCI) to boost the digital transformation of rural America. RCI is bringing edge computing’s economic and efficiency benefits to rural areas of the country.
The RCI was created by Trilogy Networks in 2020 with the goal of deploying a unified, dispersed cloud across 1.5 million square miles of rural America.
The partnership will allow farmers to receive a complete artificial intelligence-powered precision agriculture solution from Sway AI and Trilogy Networks.
“The sheer thought of the different technologies and applications a farmer has to rely on can be overwhelming, creates a barrier to entry, and also discourages sustained use,” said Co-founder and Chief Product Officer at Sway AI Jitender Arora.
He further added that they would be able to provide farmers meaningful data and complete insights for precision farming utilizing the Sway AI unified application as the RCI’s newest partner.
As technology continues to evolve at an exponential pace, agricultural software and applications are becoming more widely used as they provide impactful and valuable data. The challenge is that there is no all-in-one solution available in the market that caters to all the needs of farmers.
With this new partnership, Sway AI and Trilogy Networks will unify data and improve decision-making across the whole production process to assist farmers.
COO of Trilogy Networks, Nancy Shemwell, said, “AgTech made simple – is the key – Trilogy’s FarmGrid™ is the answer. A digital agriculture platform simplifying connectivity challenges, delivering and supporting AgTech applications on a single platform allows the grower to see and utilize the data being gathered by a variety of IoT devices.”
Product recommendation software providing company Depict.ai raises $17 million in its recently held series A funding round led by Tiger Global.
Other investors like Initialized Capital, EQT Ventures, Y Combinator, and a team of angels also participated in the latest funding round of Depict.ai.
According to the company, it plans to use the freshly raised funds to further refine its recommendation engine, expand its workforce, and also expand into other global markets like the United States and Europe.
Depict.ai’s one-of-a-kind solution can be used by any eCommerce company to provide Amazon-class recommendation features to the customers, which helps in providing an enhanced shopping experience.
To be precise, Depict.ai’s technology is a plug-and-play solution that understands all of a retailer’s products.
“Depict.ai’s AI-based product recommendation platform is completely novel because it does not require historical sales data, enables online retailers of any size to deliver high-quality recommendations, a key driver of increased revenues,” said John Curtius, Partner, Tiger Global.
He also mentioned that they are delighted to work with Depict.ai and its team as the company continues to expand into new markets.
The company’s system used deep learning technology to effectively identify product images and metadata to understand a retailer’s offer and provide a highly accurate recommendation service. An added advantage of Depict.ai’s solution is that it can offer the recommendation feature without the need for entering any previous sales data.
Sweden-based technology company Depict.ai was founded by Anton Osika and Oliver Edholm in 2019. To date, the firm has raised around $19.9 million over two funding rounds.
Founder and CEO of Depict.ai, Oliver Edholm, said, “Our ambition is to bring retailers all the AI infrastructure they need for product discovery, so they can focus on delighting their customers with great products instead of worrying about technical complexities.”
He further added that until now, AI benefits had been reserved for eCommerce tech behemoths, but they are about to change that by providing every eCommerce business with the AI technology they need to produce world-class product suggestions.
DeepMind, Google’s artificial intelligence subsidiary, has trained an AI to regulate the superheated plasma within a nuclear fusion reactor, paving the way for endless clean energy to arrive sooner.
DeepMind used its powerful deep learning technologies in partnership with the Swiss Plasma Center (SPC) at Ecole Polytechnique Federale de Lausanne (EPFL) to manipulate superheated plasma within a magnet-based reactor known as a “variable-configuration tokamak” (TCV). A tokamak is a doughnut-shaped vacuum encased by electromagnetic coils that holds a hydrogen plasma hotter than the Sun’s core. However, the plasmas in these devices are profoundly unstable and must be controlled to extract energy from the fusion reactions.
3D model of the TCV vacuum vessel. (DeepMind/SPC/EPFL)
The Swiss Plasma Center at EPFL uses tokamak to explore the best conditions for confining continually changing plasma. The form and distribution of the plasma in the tokamak can be altered by varying the voltage in the 19 magnets that keep it in place. To guarantee that, the plasma never reaches the vessel’s walls, which would result in heat loss and perhaps damage, a control system is used to coordinate the tokamak’s several magnetic coils and regulate the voltage on them thousands of times per second. However, testing new plasma configurations by altering the tokamak’s linked settings necessitates a large amount of technical and design work.
According to an article published in the journal Nature, researchers from the two organizations employed a deep reinforcement learning system to control the 19 magnetic coils within the variable-configuration tokamak at the Swiss Plasma Center. The success of this setup will pave the way to shaping the design of larger fusion reactors in the future.
The researchers achieved this accomplishment by training their AI system in a tokamak simulator, where the neural network named critic learned how to negotiate the complexity of magnetic confinement of plasma through trial and error. It all began with monitoring how different settings on each of the 19 coils influenced the form of the plasma inside the vessel.
The AI-based system was able to develop and maintain a broad range of plasma forms and advanced configurations after being trained, including one in which two independent plasmas are maintained in the vessel at the same time. Finally, the researchers put their new system through trials on the tokamak to assess how it would operate in real-world scenarios. Here, they embedded the critic’s capability in another neural network called actor, which is a smaller, quicker network that operates on the reactor itself.
The RL system shaped plasma into a variety of configurations within the reactor by regulating the SPC’s variable configuration tokamak, including one that had never been seen previously in the TCV: stabilizing ‘droplets’ where two plasmas co-existed concurrently inside the device. Some of the configurations included a D-shaped cross-section similar to what would be utilized inside ITER (previously the International Thermonuclear Experimental Reactor), the large-scale experimental tokamak under development in France, and a snowflake arrangement that might help distribute the high heat of the reaction more uniformly across the vessel.
Visualization of controlled plasma shapes. (DeepMind/SPC/EPFL)
Eventually, it successfully contained the plasma for roughly 2 seconds, which is approaching the reactor’s limits i.e., TCV can only sustain the plasma for 3 seconds in a single experiment before cooling down for 15 minutes. The record for fusion reactors is 5 seconds, which was recently established by the Joint European Torus in the United Kingdom.
According to Federico Felici of EPFL, while there are various theoretical ways that could be deployed to contain the plasma using a magnetic coil, scientists have tried-and-tested strategies. On the other hand, the AI astounded the team with its new way of creating the same plasma structures using coils. As per Felici, the reinforcement learning AI system opted to employ the TCV coils in a completely new method, which nonetheless provides a similar magnetic field. This implies it was still producing plasma as anticipated, but it was using the magnetic cores in an entirely new way because it had unlimited flexibility to explore the whole operating space.
“So people were looking at these experimental results about how the coil currents evolve and they were pretty surprised,” Felici adds.
Nuclear fusion is a process that includes colliding and fusing hydrogen, a common element in water, to power the stars of the universe. The process, which releases massive amounts of energy, has been proclaimed as a potentially unlimited source of renewable energy, but it still faces a number of technical hurdles. The sheer gravitational mass of stars is enough to force hydrogen atoms together and overcome their opposing charges. But on Earth, magnetic coils are a must to sustain a controlled reaction.
While impressive, DeepMind’s breakthrough is simply the first step toward a practical fusion energy source. According to the laboratory, simulating a tokamak takes many hours of computer processing for one second of actual time. Furthermore, because the state of a tokamak might change from day to day, algorithmic enhancements must be created both physically and in simulation.
For the time being, this initiative should open the path for EPFL to pursue future combined research and development possibilities with other firms. “We’re always open to innovative win-win collaborations where we can share ideas and explore new perspectives, thereby speeding the pace of technological development,” says Ambrogio Fasoli, the director of the SPC and a co-author of the study.
Technology giant Amazon announces that its new solar power plant in South Africa is now operational and is delivering energy and opportunities.
The 10-megawatt solar plant located in the Northern Cape province is the largest solar power plant in the country. According to Amazon, the project is planned to produce up to 28,000 megawatt-hours (MWh) of renewable energy each year, enough to power around 8,000 average South African homes for a year.
Apart from providing clean everyday electricity, the power plant will help in drastically boosting the economy of the region. The solar project is majorly-owned by black women and operated by a South African-owned enterprise.
“Energy projects that enable black investment are our surest way to a just transition to renewable energy,” said Meta Mhlarhi, an investor in the solar plant project.
The solar power plant is also a significant step forward towards the country’s 2030 renewable energy goals. According to Amazon, the solar plant’s design will prevent an estimated 25,000 tonnes of carbon emissions per year. It is a massive achievement as it is equivalent to removing 5,400 cars off South Africa’s roads.
In addition, the construction of the solar plant supported 167 employment opportunities in the local community, and it will continue to do so for maintenance, operations, and security purposes.
Director of energy at Amazon Web Services, Nat Sahlstrom, said, “Amazon is committed to working with governments and utility suppliers around the world to help bring more renewable energy projects online.”
He further added that they are honored to collaborate with the Department of Minerals and Energy, the South African National Energy Regulator, and Eskom to develop a new model for renewable energy generation in the country.
At Meta AI’s Inside the Lab event on 23 Feb 2022, Yann LeCun, Meta AI’s chief scientist, proposes that AI’s ability to approach human-like capability is a matter of the ability to learn the internal architecture of how the world works. He notes that a teenager can learn to drive in 20 hours. On the other hand, an autonomous driving system requires billions of labeled data for training and millions of reinforcement learning trials. Yet they fall short of human’s capability to drive cars. He proposes a 6 Modules Architecture of Common Sense to Achieve Autonomous Intelligence during the event.
LeCun believes that the next AI revolution will come when AI systems no longer rely on supervised learning. He hypothesizes that humans and nonhuman animals can learn about the world through observation and small amounts of interactions, often called common sense. He also said that AI systems would have to learn from the world itself with minimal help from humans, which can be achieved with common sense.
“Human and nonhuman animals seem able to learn enormous amounts of background knowledge about how the world works through observation and through an incomprehensibly small amount of interactions in a task-independent, unsupervised way,” LeCun says. “It can be hypothesized that this accumulated knowledge may constitute the basis for what is often called common sense.”
LeCun proposed an architecture of six separate, differential modules that can easily compute gradient estimates of the objective function with respect to input and propagate the information to upstream modules. This common-sense architecture can help AI systems to achieve autonomous intelligence. The six modules are configurator, perception, world model, short-term memory, actor, and cost.
The configurator module is for executive control, like executing a given task. It’s also responsible for pre-configuring the perception, world model, cost, and the actor module by modulating the parameters of those modules.
The perception module receives signals from sensors and estimates the current state of the world, but only a small subset of the perceived state of the world is relevant and valuable for a given task.
The world model module has two roles, and it’s the most complex piece of architecture. The first role is to estimate missing information about the state of the world that is not provided by perception to predict the natural evolutions of the world. The second role is to predict plausible future states of the world. The world model module acts as a simulator to the task at hand. It helps represent multiple possible predictions.
The cost module predicts the level of discomfort of the agent and has two submodules: the intrinsic cost and the critic. The former submodule is immutable and computes discomforts like damage to the agent, violation of hard-coded behavioral constraints, etc.). The latter submodule is a trainable module that predicts future values of the intrinsic cost.
The actor module computes proposals for action sequences. “The actor can find an optimal action sequence that minimizes the estimated future cost and output the first action in the optimal sequence, in a fashion similar to classical optimal control,” LeCun says.
The short-term memory module keeps track of the current and predicted world state and associated costs.
The center of this architecture is the predictive world model. Since the real world is not entirely predictable, it is critical to represent it with multiple plausible protections. The challenge is to design a model that can learn the abstract presentations of the world, ignore irrelevant details, and then predict a plausible model.
Meta AI has introduced JEPA or joint embedding predictive architecture that can capture dependencies between two inputs. JEPA can produce informative abstract presentations while eliminating relevant details while predicting the model. The idea is that JEPA will be able to learn the intricacies of the process of the world just as a newborn does by observation.
