California start-up Cerebras has developed the world’s biggest computer chip to be used to train AI systems. It is set to be revealed after being in development for four years.
Contrary to the normal progression of chips getting smaller, the new one developed by Cerebras has a surface area bigger than an IPad. It is more than 80 times bigger than any competitors, and it uses a large amount of electricity.
The new development represents the astounding amount of computing power that is being used in AI. Included in this is the $1bn investment from Microsoft into OpenAI that was announced last month. OpenAI is trying to develop an Artificial General Intelligence (AGI) which will be a giant leap forward, something that will change much of what we know.
Cerebras is unique in this field because of the enormous size of their chip. Other companies endlessly work to create extremely small chips. Most of our advanced chips today are assembled like this. According to Patrick Moorhead, a US chip analyst, Cerebras basically put an entire computing cluster on a single chip.
Cerebras is looking to join the likes of other companies like Intel, Habana, Labs, and the UK start-up Graphcore. They are all building a new generation of specialized AI chips. This development in AI chips is reaching its biggest stage yet as the companies are going to start delivering the first chips to customers by the end of the year. Among the companies, Cerebras will be looking to be the go-to for massive computing tasks that are being done by our largest internet companies.
There are many more companies and start-ups involved in this space including Graphcore, Wave Computing, and the Chinese based start-up Cambricon. They are all looking to develop specialized AI chips used for inference. They want to take a trained AI system and use it in real-world scenarios.
Normally, it takes a long time for the development process to finish and actual products be shipped to people and companies. According to Linley Group, a US chip research firm, there are a lot of technical issues that are time-consuming. Although it takes awhile for products to be developed, there is still a big interest in these companies. Cerebras has raised over $200m in venture capital. As of late last year, they were valued at about $1.6bn. There is a lot of projected growth for the global revenue of these deep learning chipsets.
The reason that these companies are focusing on this type of processor for AI is because of the huge amounts of data that are needed in order to train neural networks. Those neural networks are then used in deep-learning systems and are responsible for things such as image recognition.
The chip from Cerebras is a single chip made out of a 300mm diameter circular wafer. It is the largest silicon disc to be made in the current chip factories. The norm is for these wafers to be split up into many individual chips instead of one giant one. Anyone who tried before ran into issues with putting circuitry into something so big. Cerebras got past this by connecting the different sectors on the wafers. Once this is done, they are able to communicate with each other and become a big processor.
Cerebras is looking forward and will try to link cores in a matrix pattern to be able to communicate with each other. They want to connect 400,000 cores while keeping all of the processing on one single chip.
It will be exciting to see these developments move forward with Cerebras and other companies continuing to advance our AI systems.
Industrial Robotics Company ABB Joins Up With AI Startup Covariant
The AI startup Covariant and the industrial robotics company ABB will be partnering to engineer sophisticated robots that can pick up and manipulate a wide variety of objects. These robots will be used in warehouses and other industrial settings.
As Fortune reported, the industrial robotics company ABB is primarily involved in the creation of robotics for car manufacturers, but the company wants to branch out to other sectors. ABB is aiming to become involved in logistics, where its robots will be used in large warehouses, such as those run by Amazon, to manipulate items, package goods, and make shipments.
According to ABB president Sami Atiya, according to Fortune, ABB sought partners that were experienced in the creation of sophisticated computer vision applications. While the company currently uses computer vision algorithms to operate some of its robots, ABB aimed to push the envelope and create reliable, high-dexterity robots capable of maneuvering and manipulating thousands of different objects.
The company examined many different companies before settling on Covariant as its partner. Covariant is a robotics research company whose researchers come from places like OpenAI and the University of California Berekely. Covariant managed to produce the only software examined by ABB that could reliably recognize many different items without the intervention of human operators.
The computer vision and robotics applications developed by Covariant were trained with reinforcement learning. Thanks to deep neural networks and reinforcement learning, Covariant was able to create software that learns through experience and is able to reliably and consistently recognize objects once a pattern has been learned. The CEO of Covariant, Peter Chen, was interviewed by Fortune. Chen explained that as more robotics companies like ABB brain out into new industries and markets, the goal becomes the creation of robots capable of a wider variety of tasks than those currently used in many manufacturing and logistics operations. Most of the robots employed in industrial capacities are only capable of doing a handful of very specific things. Chen explained that the goal is to create robots capable of adaptation.
As a result of the partnership with Covariant, ABB will get insight into the technology that drives Covariant’s AI, and this knowledge could help them better integrate AI into the tech that powers their existing robots. Currently, Covariant is a fairly small operation with only a handful of robots in full-time operational status, spread out across industries like the electronics industry, the pharmaceutical industry, and the apparel industry. However, its collaboration with ABB could cause it to see substantial growth.
The partnership between Covariant and ABB highlights the increasing role of AI startups in the robotics field. Other examples of AI startups collaborating with robotics companies includes the Japanese corporation IHI establishing a partnership with the AI startup Osaro. The joint collaboration also concerned the use of robots to grasp and manipulate objects.
While there is currently a lot of focus on robots automating away human jobs, in some industries there simply aren’t enough humans to do those jobs, to begin with. A recent report about the logistics sector estimates that over half of all logistics companies will face staff shortages over the course of the next five years. There will be a particular shortage of warehouse workers over the next half-decade. The report suggests that causes of the labor shortage within the logistics industry are falling unemployment rates, long hours, tedious work, and low wages.
AI “Maths Robot” Helps Manage Microclimates and Increase Berry Yield Predictions
One of the biggest agriculture/horticulture companies in Australia is Costa Group, and the company has recently employed an AI system intended to improve crop quality and yield by helping the company analyze its berry crops. As reported by ZDNet, the system that Costa Group employs was designed by The Yield, an AgTech company based in Sydney. The AI system analyzes 14 different features in order to derive meaningful insights. These features include temperature, soil conditions, wind, light, and rain. The information is then combined with an existing dataset and predictions about individual crops are returned.
Costa Group operates several berry farms located throughout Queensland, New South Wales, and Tasmania. The berry farms in these locations contain polytunnels, and these polytunnels have their own microclimates. Because the climate of these tunnels is controlled, they require their own “weather service”. Internet of Things (IoT) devices within the tunnels collect a wide variety of data that is fed into the AI model. The process is one of continual model creation, production, feedback, and refinement. The creators of the system describe it as a “maths robot”.
Similar AI models have been used to predict crop yield for spinach, lettuce, and other crops, yet the founder of The Yield, Ros Harvey, explained that their system is critical because berries are challenging to monitor as they grow. Unlike other vegetables or fruits, berries often go through a variety of stages very quickly and a single berry crop can have many growth stages at the same time. As Harvey explained to ZDNet:
“It’s been such a difficult problem for berry producers globally because unlike other crops, berries have many growth stages all at the same time… If you look at a berry plant, it’s fruiting, flowering, there are berries that are ready, and there are berries that are half produced because it continually fruits when it’s in season. Whereas other crops go through this linear growth stage where you harvest once at the end of the season.”
Currently, AI is typically used for just a few different applications in the AgTech industry. Among these applications are precision farming, agriculture robots, livestock monitoring, and drone analytics. In 2018, precision farming accounted for around 35.6% of AI usage in the agricultural sector. Applications like the type developed by The Yield, which assist farming operations in increasing yield and shielding themselves from risk by gaining valuable insight into growing trends, seem poised to see much more use in the near future.
The data returned by the AI system allows for the Costa Group to gain a better understanding of the yield, which in turn helps the company manage its logistical costs and price point. Harvey predicts that in the future more and more companies will begin using AI-powered applications to quantify yield and reduce risk, noting that as climate change makes weather more unpredictable more companies may choose to use polytunnels as well. The use of AI across the entire agricultural industry is predicted to grow rapidly in the near future. Machine learning, computer vision, and predictive analytics are helping agricultural operations increase yield and do more with less.
As a recent report released on the state of AI in agriculture found, AI AgTech is expected to grow dramatically over the course of the next five years. In 2018, the AI market in agriculture was valued at around 330 million USD, yet it is expected to reach a value of approximately 980 million USD by the end of 2024. Other recent applications of AI in the agriculture sector include small robots designed to weed fields and keeping track of growing conditions in vertical farming operations.
Smartphone Data Combined With AI To Help Stop Vehicles From Hitting Pedestrians
Every year, hundreds of thousands of people die in accidents involving motor vehicles. Recently, an AI startup called VizibleZone devised a method to possibly prevent some of these deaths. As reported by VentureBeat, VizibleZone’s AI-powered system integrates data collected by both motor vehicles and smartphones in order to alert drivers to the potential location of a pedestrian, which can help drivers avoid tragic accidents.
According to the World Health Organization, in 2018 around 1.5 million people were killed in road accidents. More than half of all of the deaths associated with these accidents involved a collision between a pedestrian or cyclist and a motor vehicle. Over the past decade, consumer vehicles have become more high-tech and sophisticated, equipped with cameras, radar, and lidar, which are capable of detecting people near or on a road. However, a major cause of many fatal accidents is the “hidden pedestrian” problem, named for instances where a pedestrian is obscured by an object until it’s too late.
VizibleZone devised a potential solution to this problem, making use of data from both smartphones and smart cars to create representations of city streets that pinpoint possible locations for both cars and pedestrians. If an AI model determines that there is a potential collision hazard, it will warn the driver of the vehicle, who can take the appropriate action to avoid a collision.
According to VentureBeat, Shmulik Barel, cofounder of VizibleZone, the applications based on their software development kit work by collecting large amounts of sensor and GPS data, which is anonymized before use. Hundreds of thousands of individuals contribute their data to a database used to train the main AI algorithms, which create behavioral profiles that take the environment surrounding these individuals into account. While the model’s assumptions about constant properties like the size of objects and vehicles might be generalizable, the model must be customized to fit the individual environment that the applications operate in. This is because drivers and pedestrians display different behavior in different regions of the globe. In order to make the model reliable, these regional differences in behavior must be accounted for.
Once the behavioral profiles are constructed and fine-tuned, those who elect to use the app just allow the app to broadcast their location. The broadcast information is received by vehicles making use of Viziblezone’s software. An AI model then calculates the probability of an accident occurring based on variables like road conditions, the driver’s profile, and the pedestrian profile. If the risk of an accident exceeds a certain threshold, the driver will be alerted to the potential of an accident approximately 3 seconds in advance.
Barel explained that the system is capable of alerting the pedestrian to a possibly dangerous approaching vehicle as well if the user wishes to receive those notifications. The AI system is reportedly capable of detecting passengers approximately 500 feet away, or 150 meters away, in any weather conditions and at any time of day. One concern is the fact that the app seems to drain battery life by approximately 5% every 24 hours, although the startup is currently attempting to reduce that energy usage by half.
According to Barel, as interviewed by VentureBeat, Uber has discussed the possibility of incorporating VizibleZone’s technology into its ride-hailing services. While collaborating with Uber might give VizibleZone a big break, the company’s current focus is improving the accuracy of the system by scaling up the number of devices that are networked together. VizibleZone would also like to integrate its technology with other smart devices and city infrastructure, such as traffic lights.
While devices like radar, lidar, and cameras have managed to cut down on many accidents, there still hasn’t been an application capable of tackling the “hidden pedestrian” problem. If VizibleZone can successfully adapt its current model and bring it to more places around the world, many lives could potentially be saved.