Carnegie Mellon University (CMU) announces the launch of its new artificial intelligence maker space named JPMorgan Chase & Co. AI Maker Space. The newly launched maker space for AI aims to provide a platform where students can experiment with artificial intelligence projects in the field of computer vision, speech recognition, robotics, machine learning, and other related domains.
The head of Carnegie Mellon University’s UG program in artificial intelligence, Reid Simmons, has been appointed as the director of JPMorgan Chase & Co. AI Maker Space. The maker space is built across an area of over 1900f2and is situated on the first floor of the university’s Tepper building.
The building also hosts CMU’s Swartz Center for Entrepreneurship and business school. Simmons said, “We want students from all over the university — from engineering, business and fine arts — to come and use their creativity to make interesting things happen.” He further added that the maker space would give students the freedom to let their imaginations run wild.
JPMorgan Chase & Co. AI Maker Space will allow students to bring innovations that would revolutionize the artificial intelligence industry. An autonomous robot developed by Fetch Robotics cut the ribbon to inaugurate the maker space. This unique way of launching the facility shows the innovations it will bring to tackle some of the greatest challenges in the field of artificial intelligence.
The facility has been funded by the United States-based JPMorgan Chase & Co. President of Carnegie Mellon University, Farnam Jahanian, said, “By putting critical AI tools directly into the hands of CMU students and researchers at the forefront of innovation, the (maker space) will empower our students to become the next generation of AI leaders.”
He also mentioned that the facility will allow students to experiment with hands-on tools in an open environment to ‘sharpen the cutting edge of AI.’
The Indian Institute of Technology (IIT) Jodhpur, one of India’s prestigious institutes, is in the process of drafting its plan to launch a new Artificial Intelligence of Things (AIOT) Hub. The new AIOT Hub would line up academics with real-life and industrial-scale issues resulting in enriching the innovative ecosystem of IIT.
The AIOT hub is an amalgamation of artificial intelligence and the Internet of Things (IoT) with mobile communication technology. AIOT, which is the flawless concoction of AI and IoT, is set up to achieve more efficient AI, improve human-machine interactions, and intensify data management.
The hub is designed with vital characteristics like self-learning and self-healing human-machine workflows, self-ruling decision making with edge computing, interconnected and intelligent devices.
“The proposal of converging IIT Jodhpur academics with real-life and industrial-scale problems was first laid by The AIOT Innovative Hub and JOKIC (Jodhpur city knowledge and innovation cluster),” said IIT Jodhpur director Santanu Chaudhury.
He also added that they are expecting the trigger of local industrial development through start-ups and new generation MSMEs who are eager to take forward the agenda of Atmanirbhar Bharat.
The AIOT Innovation Hub at IIT Jodhpur Innovation Complex is expected to create an ecosystem for the co-creation of AIOT technologies and products by faculty, students, and industry partners. This would help in lining up business partners for possible consumption of products, ensuring feasible business prospects.
NIT Warangal has introduced a five-day online course on AI exclusively for the faculty group to keep them abreast of the recent developments in AI and ML. This course covers major topics related to AI, including data visualization and deep learning.
The program targets to fetch a good understanding of these subjects and is worth attending as professionals of prominent academies like IIT’s, NIT’s, Central universities, and Industrial professionals will deliver lectures.
Apart from the faculty members, doctrinal research students, and participants from industries are also invited to this program. The partakers will get introduced to the latest tools of AI and ML. The participants will gain good knowledge in solving real-world problems in AI and ML applications on completing this program.
The authorities declared that the online course commences from November 22, for the next four days, until November 26. The time period allotted for the course is 3 hours a day between 2 pm to 5 pm.
The official information declared by the NIT authorities states that they will hold up the online registration process till November 15. The fee for the same is Rs 1000 for participants from industries and Rs 500 for the faculty members, doctoral research students, and postdoctoral fellows.
The photo app PHHHOTO has filed an antitrust petition against Meta (formerly known as Facebook) for apparently copying its features for Instagram. It also claimed that this isn’t the first antitrust case against Facebook. The company affirmed that Facebook showed interest in working with it and has facsimiled its content, thereby hiding its name from search results, which kicked off PHHHOTO out of business.
“The Phhhoto app created short GIF videos that were published by Facebook in the name of “Boomerang” are not the actual creation of Facebook,” said the company. The app additionally has the feature of clubbing up all five photos into a single frame that can be shared on Instagram. Facebook has been further accused of blocking Phhhoto from Instagram’s API.
Phhhoto app, which was set up in 2014, has to cease its operations by 2017 even though it has more than 3.7 million monthly users on its onset, including BBeyoncé, Joe Jonas, Chrissy Teigen, and Bella Hadid.
“The actions of Facebook and Instagram destroyed Phhhoto as a viable business and ruined the company’s prospects for investment. Phhhoto failed as a direct result of Facebook’s anticompetitive conduct,” Phhhoto said in the complaint filed in US District Court.
It also added that Phhhoto was established to grow into a massive social networking giant with similar capabilities as other social networking and media companies with which Facebook did not interfere.
Though Phhhoto is demanding Facebook for its monetary damages, the Facebook officials informed The Verge that “this suit is without merit and they would shield themselves in the court.”
The Pune Municipal Corporation (PMC) intends to use artificial intelligence to resolve community issues such as garbage dumping. The Pune Civic Administration has conducted a pilot project at Yerawada to deal with difficulties like littering public spaces.
Pune’s civic body has identified at least 150 public spots where garbage dumping is prevalent with AI. PMC intends to use AI to find and fine citizens that are defacing public places by littering.
Officials said that the civic body also plans to use AI, data analytics, satellite imagery, machine learning (ML), and e-governance to regularise illegal constructions, property tax, etc. Along with fighting trash disposal, PMC plans to take the help of AI and data analytics to collect property tax.
Kunal Khemnar, additional commissioner, PMC, said that the chronic spots for garbage disposal would be identified using technology, and the PMC would carry out mitigation programs after that.
“The civic administration plans to use mechanised sweeping machines at chronic spots. The aim is to reduce human intervention in garbage collection and disposal,” said Khemnar. He also added that software had been developed for keeping a watch on the garbage disposal.
PMC uses AI to curb open dumping to ensure a clean and hygienic city. All the spots identified using satellite images will be cross-verified by staffers. “Even if garbage dumping does not stop after repeated cleaning, it will take action against those throwing trash. They will identify the offenders with the help of CCTV footages. They will be fined,” said a senior PMC official.
NeuReality, a semiconductor company located in Israel that is developing the next generation of AI-centred computing system architecture, has partnered with IBM to produce the next generation of high-performance AI inference platforms. These systems, according to both companies, will yield significant cost and power savings for deep learning use cases. This follows NeuReality’s public debut in February when the company raised $8 million in a seed round to accelerate AI workloads at scale.
According to the official announcement, this alliance will enable important industries including banking, insurance, healthcare, manufacturing, and smart cities to implement computer vision, recommendation systems, Natural Language Processing, and other AI use cases. They also say that the partnership would hasten deployments in today’s rapidly expanding AI use cases, which are currently available in public and private cloud data centers.
AI Inference is an emerging buzzword in the artificial intelligence domain. It is the aspect of AI where neural networks are really employed in real-world applications and generate results. While high-performance AI inference systems are becoming a rising area of attention for businesses, they also promise to minimize the cost and power consumption of deep learning.
The agreement covers NR1, NeuReality’s first AI-centric architecture Server-on-a-Chip ASIC implementation. The NR1 prototype platform is built on NeuReality’s first-generation FPGA-based NR1-P prototype platform, which was unveiled earlier this year in May. According to the company, with features like native AI-over-Fabric networking, full AI pipeline offload, and hardware-based AI hypervisor, NR1-P can eliminate system bottlenecks in today’s solutions. In addition, it will also lower the cost and power consumption of inference systems and services. Prior to the release of the NR1 production platform next year, the NR1-P platform will offer software integration and system-level validation.
As part of the newly formed deal, IBM will become a NeuReality design partner, working on product requirements for the NR1 chip, system, and SDK, which will be incorporated in the architecture’s next edition. The two businesses will jointly examine NeuReality’s solutions for usage in IBM’s Hybrid Cloud, covering AI use cases, system flows, virtualization, networking, security, and other areas.
“Having the NR1-P FPGA platform available today allows us to develop IBM’s requirements and test them before the NR1 server-on-a-chip’s tapeout,” said NeuReality CEO Moshe Tanach. “Being able to develop, test and optimize complex data center distributed features, such as Kubernetes, networking and security before production is the only way to deliver high quality to our customers.”
The basic NR1-P architecture is based on a 4U server chassis with 16 Xilinx Versal Adaptive Compute Acceleration Platform (ACAP) cards. According to NeuReality, the NR1-P platform, which is intended for usage in cloud data centers and edge nodes, is now being shown to clients and partners and will be followed by further implementations. Customers may incorporate it in orchestrated data centers and other facilities and witness it in action on the new prototype platform, which is being used to test the technology.
IBM opened the IBM Research AI Hardware Center in Albany, New York, in February 2019 to build a worldwide research hub for developing next-generation AI hardware with multiple technological partners and expanding the company’s cooperative nanotechnology research activities. NeuReality will become the IBM Research AI Hardware Center’s first start-up semiconductor product member and a licensee of the Center’s low-precision high-performance Digital AI Cores as a result of this cooperation.
Dr. Mukesh Khare, Vice President of Hybrid Cloud research at IBM Research, said, “In light of IBM’s vision to deliver the most advanced Hybrid Cloud and AI systems and services to our clients, teaming up with NeuReality, which brings a disruptive AI-centric approach to the table, is the type of industry collaboration we are looking for.” Mukesh added that the partnership with NeuReality is expected to drive a more streamlined and accessible AI infrastructure, which has the potential to enhance people’s lives.
DeepMind, a London-based artificial intelligence research company owned by Google’s parent Alphabet, has introduced a new drug development company.
Source: Isomorphic Laboratories
The tech juggernaut and Google parent firm had made a big and unexpected splash in the realm of biology last year. Last November, DeepMind, the company’s AI research arm, astonished structural biologists by solving the long-standing challenge of protein structure prediction with their deep learning model, AlphaFold2.
This discovery was a crucial milestone in the drug discovery industry as the function of proteins in a cell is linked to almost every illness. The three-dimensional protein structure, in turn, may be used to identify the function. Anyone who understands this “folding” may better identify and treat illnesses, among other things.
AlphaFold 2 solved a 50-year-old protein folding puzzle by accurately predicting a protein’s 3D structure directly from its amino acid sequence. Its goal is to learn more about how a protein’s delicate structure interacts with cells and how their unique forms might drive both life and sickness. Eight months later, it expanded on those results by making the model’s code and a database of over 350,000 predicted protein structures available to the public. That information has been utilized by independent researchers to speed up a wide spectrum of biological study, including efforts to comprehend the coronavirus.
Other companies under Alphabet’s wing are already researching various areas of human health. For instance, Verily monitors glucose levels in diabetics, and Calico, which is committed to exploring ways to slow down aging.
Hassabis will help Isomorphic Lab during its early operations to ease collaboration between the new company and Deepmind. He went on to say that this would also help define Isomorphic Labs’ strategy, vision, and culture. It might make use of DeepMind’s protein structure research to find out how various proteins interact. Instead of developing its own medications, the company may choose to market its models by forming alliances with pharmaceutical industries.
Hassabis noted that AI technologies would increasingly be employed not merely for evaluating data, but also for building effective predictive and generative models of complicated biological processes. According to him, AlphaFold2 is a significant first step in this direction, but there’s a lot more to come.
The new company was given the moniker “Isomorphic Laboratories” because it was assumed that information systems and biological systems might share a structured architecture. Isomorphism refers to the fact that they have similar shapes yet differ in origin. The ultimate goal of the new company is, to model and understand some of the fundamental mechanisms of life.
According to the National Center for Biotechnology Information in the United States, the cost of developing a new medicine is on average 1.3 billion dollars. The report also states that researchers currently physically create each component before testing it in the lab under human-like settings. Testing would be faster, safer, and less expensive if AI was used. Hassabis elaborates that artificial intelligence can speed up and improve the entire process by not only analyzing data but also building predictive and generative models of highly complicated biological occurrences.
Isomorphic Labs is not the first nor only company that plans to streamline drug development by leveraging artificial intelligence. Similar technology is being investigated by several notable research institutes, including a team at the University of Washington. Like Atomwise in San Francisco and Recursion Pharmaceuticals in Salt Lake City, several start-ups are seeking to use new artificial intelligence approaches to drug discovery.
Isomorphic Labs is now looking to employ a “world-class interdisciplinary team,” which would include professionals in artificial intelligence, biology, medicinal chemistry, biophysics, and engineering, all of whom would work in a highly collaborative and inventive setting. As the company progresses into later phases, Hassabis also says that it may appoint a new CEO.
Less than a year after closing a $500 million funding round led by T. Rowe Price Associates, autonomous delivery startup Nuro raises $600M. The money will be used to hire more employees and enhance its technology. In this new funding round, the lead investor was Tiger Global alongside Google LLC, SoftBank Group Corp., and several others. Nuro is now reportedly valued at $8.6 billion.
Nuro’s growing fleet of autonomous delivery vehicles can deliver groceries and medicines from shops or retail locations to consumers’ homes. As a part of a partnership with Kroger Co, Nuro’s vehicles have carried out thousands of deliveries. Neuro teamed up with FedEx corp earlier this year to evaluate whether its vehicles are suitable for parcel logistics.
Nuro’s autonomous delivery vehicle, called the R2, uses an artificial intelligence driving system and several sensors to collect data about the environment for navigating the roads. Each vehicle can ferry 400 pounds of merchandise.
The design of R2 plays a significant role in pedestrian safety. The vehicle is smaller than a car and has a specialized panel to protect pedestrians in the event of a collision. The vehicle also has a redundant braking and control system that allows it to continue operating if a component malfunction.
Nuro is expanding its manufacturing capabilities; the startup announced its plan to invest 40 million dollars in a test track and a new manufacturing facility. The startup aims to build vehicles faster, and the current funding round could help advance its expanding plans. The proposed test track will help the company boost the startup’s development effort by providing a safe environment to test R2’s autonomous driving software. Testing is essential because machine learning systems improve through training, and practice runs on simulated roads will improve R2’s navigation.
Additionally, Nuro is partnering with Google to enhance its autonomous driving software. The company announced a five-year strategic partnership with Google Cloud that will extend to data storage and running vehicle simulation workloads. Google’s collaboration with Nuro is notable given its sister company Waymo is also developing fully autonomous cars and vehicles.
On 1st November, DataRobot announced the acquisition of decision.ai and the return of Dan Becker to DataRobot. Dan’s legendary career has taken him from Kaggle to DataRobot to Google to founding decision.ai, and now, with this acquisition, he is back to DataRobot. Dan has experience consulting on AI projects for 6 Fortune 100 companies and contributions to leading open-source AI tools, including Keras and TensorFlow.
DataRobot was founded in 2012, and today, it is the AI Cloud leader, delivering a unified platform for all data types, users, and environments to speed up the delivery of AI to production for every organization.
Earlier this year, DataRobot launched a No-Code App Builder. Regardless of the user’s technical expertise, it allows any user to quickly turn a deployed model into a rich AI application without coding. Users can also get context on critical features, run what-if simulations, and determine how to optimize their models for precise outcomes. With DataRobot’s Decision Intelligence Flows capability launch, users can visually create complex rules to assess their predictions. It also helps to automate the decision-making process at scale.
“This gives DataRobot a chance to integrate some key complementary technology and bring onboard some great minds such as Dan Becker & team. This was an excellent decision (pun intended) by DataRobot,” said Igor Veksler in a LinkedIn post.
The acquisition of Decision.ai will help DataRobot extend and improve its existing no-code app building and decision intelligence flow tools. These tools help DataRobot users optimize decisions in complex, dynamic business processes. With the augmented Decision Intelligence Suite in DataRobot AI Cloud, companies can make faster and smarter decisions when they can see the entire picture and how the decision will play out over time.
Frontier Development Lab (FDL) researchers along with Intel AI Mentors conducted a landmark astronaut health study to adequately understand the physiological effects of radiation exposure on astronauts. The SETI Institute hosts the Frontier Development Lab in the U.S. It is in a public/private partnership with private-sector companies, NASA, and commercial AI partners. Frontier development lab used Intel artificial intelligence technology to create a first-of-its-kind algorithm to identify cancer progression biomarkers using mouse and human radiation exposure data.
Cosmic radiation can lead to health problems and cause cancer complications since it can penetrate several layers of aluminum and steel layers and affect human tissue during space travel. Since there is little data on how cosmic radiation affects astronauts from existing space missions, they need to access soiled data from various institutions that are heavily protected. FDL’s casual machine learning models don’t have to move the data between physical locations to operate on data across different areas.
Shashi Jain, strategic innovation and FDL partner manager at Intel, said that “We believe that the FDL Astronaut Health challenge results will enable NASA to understand the mechanisms involved in protecting astronauts more effectively as we return to the moon and beyond, as well as provide a blueprint to accelerate the use of AI in healthcare applications on Earth.”
Intel and FDL’s casual machine learning allows a federation of collaborator institutions to allow access to the data without moving it to separate locations. This is a testament to how public and private institutions can work together to unlock more insights that would otherwise remain buried.
FDL’s CRISP 2.0, developed by extending CRISP 1.0, leverages Intel’s Open Federated Learning framework (OpenFL). It makes it possible to drain and combine FDL’s CRISP 2.0 from institutions such as Mayo Clinic, NASA, and NASA GeneLab without moving the data to a central location.
