Robin Li Yanhong, the co-founder and CEO of Baidu, predicts eight artificial intelligence technologies that will shape the future of our society in the next decade.
He spoke about next-generation artificial intelligence technologies at the ABC summit 2021 held in Beijing on 29th July. Li believes that humankind would witness a quantitative and qualitative technological transformation in the coming years.
He mentioned technologies like machine translation, biological computing, autonomous driving vehicles, digital city operation, deep learning frameworks, artificial intelligence-powered chips, knowledge management systems, and intelligent personal assistants would play a vital role in this revolution.
“At present, the world is ushering in a new round of innovation. The intelligent economy with AI as the core driving force has become a new engine for economic development. With the industry and society being more and more aware of the true value of AI, AI technology has entered a period of rapid application after long-term investment and accumulation,” said Robin Li.
He further mentioned that cross language real time communication earlier portrayed in movies is now becoming a reality. Li’s company Baidu had earlier worked with the Chinese government to develop a 3D artificial intelligence training system that helped divers to train more precisely with motion capture and data analysis features.
Experts believe that new technologies will help people in every walk of life and will also boost economic development on a global scale. Baidu is also developing a cloud-based artificial intelligence chip named Kunlun 2 that will enter the mass production stage by the end of this year.
Chief Technology Officer of Baidu, Wang Haifeng, said, “We are in the best age of technological innovation and industrial development.” He also added that advanced technologies would bring both digital transformation and intelligent up-gradation to increase economic growth.
Fitness application developing startup Insane AI raised $873,000 in its seed funding round led by pi Ventures. Investors like Anupam Mittal, Sameer Pithawalla, Soumil Majumdarm, Karan Tanna, Arjun Jain, and Lets Venture participated in the funding round.
Insane AI is a Bengaluru-based tech startup that specializes in developing artificial intelligence-powered fitness applications to help customers achieve their fitness goals. The firm was founded in early 2021 by Anurag Mundhada, Jayesh Hannurkar, and Sourabh Agarwal.
The company also provides a mobile app that gives the users information and knowledge about artificial intelligence, machine learning, and data science. The smartphone application is available for free download on Google Play Store.
Co-founder Anurag Mundhada said, “Mainstream fitness formats can become drab and don’t challenge or inspire people enough to really build a long-term habit of fitness. Our unique gamified workout format keeps users highly engaged and motivated to give their best during every session, allows them to monitor their progress, and keeps them committed to their fitness goals.”
The company noted a 50% increase in downloads after the COVID-19 pandemic as all the gyms and fitness centers are closed. The company’s platform integrates fitness and gaming services using machine learning and artificial intelligence, augmented reality, and computer vision to provide an immersive experience to the users.
The application tracks the user’s sleep routine, nutrition intake, mental health, and physical fitness to give exercise suggestions.
Shubham Sandeep of pi Ventures said, “Digital health and fitness has seen a rising demand amidst the pandemic and is already a multi-billion dollar industry. However, the at-home fitness experience is lacking, which is waiting to be disrupted by technological advances such as artificial intelligence, aurgmented reality, and computer vision.”
He further added that they have complete faith in Insane AI for developing solutions to reimagine the fitness industry.
The Michael J. Fox Foundation (MJFF) and the research arm of IBM have developed an AI model that can group typical Parkinson’s disease (PD) symptom patterns. This AI model can accurately identify the progression of these symptoms in a patient, regardless of whether or not they are taking medications to mask those symptoms.
This means that in the future, doctors will be capable of utilizing AI to predict how their patients’ diseases will proceed, allowing them to manage their symptoms better. This was one of the primary goals the two organizations had set out to achieve from the start, according to the discovery results published in The Lancet Digital Health.
The human motor system uses a sequence of distinct movements to accomplish bodily activities, e.g., arm swinging when walking, running, or jogging. These movements and the transitions between them produce activity patterns that may be monitored and examined for indications of Parkinson’s disease. Researchers study the physical measurements obtained from Parkinson’s patients, which differ from those taken from non-patients, and the development in those differences over time indicates disease progression. But it was unknown why some people with Parkinson’s will have their disease turn more severe than others until the recent breakthrough by Big Blue.
Since July 2018, IBM Research and MJFF have been collaborating to see how machine learning may be used to assist physicians in better understanding the underlying biology of Parkinson’s disease, especially given how it proceeds so differently from person to person. This machine-learning algorithm evaluated data from patients over seven years and identified trends in their symptoms that were connected to neurodegeneration. Based on this insight, the team created an AI computer model that might help doctors anticipate how a patient’s condition would evolve, aiding them in giving the correct treatments at the right time or deciding who would benefit the most from a clinical trial. The researchers identified eight distinct states in Parkinson’s disease, each containing both motor and non-motor symptoms, and discovered that the disease might shift between them in no particular sequence over time. One of these states included severe cognitive impairment.
IBM stated that its aim is to use AI to help with patient management and clinical trial design. “These goals are important because, despite Parkinson’s prevalence, patients experience a unique variety of motor and non-motor symptoms,” the company added.
The Michael J. Fox Foundation’s Parkinson’s Progression Markers Initiative provided the data for this research. It is a clinical study originally started in 2010 in cooperation with more than 30 biotech, pharmaceutical, non-profit, and private firms. The study, which included over 1,400 patients from throughout the world, has gathered years of patient data from health records, wearable devices, and cellphones, as well as sequencing genomes and analyzing specimens obtained during their condition. According to IBM, this is the largest and most robust volume of longitudinal Parkinson’s patient data to date.
The findings were compared to those of a control group of 610 Parkinson’s disease patients from the National Institute of Neurological Disorders and Stroke Parkinson’s Disease Biomarker Program (PDBP). This aided in the validation of the AI model that IBM researchers had been working on since mid-2018.
Messaging service provider Gupshup raises $240 million in its additional funding round on Wednesday. Investors like Tiger Global, Fidelity Management, Think Investments, Malabar Investments, Harbor Spring Capitals, and White Oak participated in Gupshup’s series F funding round.
Earlier this year, the startup raised nearly $100 million. The fresh funding has increased Gupshup’s market valuation to $1.4 billion. The company plans to use the funding to expand its market reach and to develop its business messaging platform further.
Co-founder and CEO of Gupshup, Beerud Sheth, said, “Conversation is becoming a bigger part of doing business, and it has partly been driven by the pandemic. Second, we have always been the leader in this space, but the product innovation we have focused on in the last two to three years has worked in our favor.”
He further mentioned that new investors would provide crucial insights to the company for it to plan its future strategies. The startup is also planning to use a certain amount of funds for a share buyback for its loyal employees and investors.
Gupshup is a San Francisco-based company founded by Dr. Milind R Agarwal, Beerud Sheth, and Rakesh Mathur in 2004. The firm offers a conversational messaging platform to businesses to share short messages privately and publicly.
The platform delivers more than 4 billion messages every day and has sent a total of 150 billion messages till date. Sheth said, “There was still more investor interest, and the company wanted to build relationships with the public market investors. So, having a relationship with them now will help us in doing an IPO later.”
Company officials claim that they have witnessed a 60% increase in growth compared to the previous year. Investors firmly believe that the new funding will help the company’s team to scale up its operations and fill the product gaps in its portfolio.
Climate change is leading to intense and deadly wildfire seasons, stretching the firefighting resources to the limit. However, analytical tools like Suppression Difficulty Index (SDI) are helping firefighters to improve their chance of reducing wildfires by bringing machine learning and big data into the picture. Firefighters are turning to AI tools for building control lines and agendas.
For decades, fire managers have been relying on weather patterns and analytical data on fire behavior. Now, they are utilizing predictive technologies and artificial intelligence to plan wildfire management schedules and manage analytics of their terrain in real-time.
Researchers like Mr. Dunn hope their ML models and tools can ensure that the scarce fire resources are deployed as efficiently as possible. Firefighters are currently using Potential Operational Delineations (PODs), a popular tool that Mr. Dunn helped develop. PODs use advanced spatial analytics that allows teams to plan the location to take on wildfires even before they break out. The POD tool superimposes several statistical models like SDI over a map of a region. This aids fire managers in planning out their control lines and plans of attack in advance.
“You will never take the personal element out of fighting fires, but people make bad decisions under stress – they can’t crunch all this data on their own. This is about reducing the uncertainty, and helping firefighters make better decisions,” said Brad Pietruszka, who has been using analytical tools like PODs since 2017. He is a fire manager at San Juan, a 1.8-million-acre National Forest.
Another complex tool is the Potential Control Locations (PCLs) algorithm, which suggests where to build control lines during a fire. It considers information about ridges, flat grounds, fuel present in the ground, geography, distance from roads and public spaces, and samples it across historical fire perimeters.
Tools like SDI, PODs, PCLs, and others provide crucial information to firefighters during out-of-control wildfire seasons. As firefighters turn to AI to fight wildfires, researchers have stressed that these tools are efficient only when coupled with insights from people living in wildfire-prone areas.
Tech giant Microsoft acquires artificial intelligence startup Suplari that specializes in developing solutions for corporate expenditure and cash flow analysis.
Suplari’s platform is also capable of predicting fund inflow and trend tracking. With this acquisition, Microsoft plans to integrate Suplari’s artificial intelligence-powered platform with its cloud-based Dynamic 365 solution.
The merger would help businesses to maximize financial visibility by analyzing current data and previous trend patterns from various data sources using artificial intelligence technology.
According to Microsoft, more than half of the businesses worldwide would use artificial intelligence cloud services for financial analysis needs by the year 2022.
Vice President of Dynamic 365 at Microsoft, Frank Weigel, said, “Together with Dynamics 365, the Suplari Spend Intelligence Cloud will help customers maximize financial visibility by using AI to automate the analysis of current data and historical patterns from multiple data sources. This acquisition will further empower Microsoft to help our customers turn data into actionable insight.”
He further added that this advancement would help enterprises to move beyond transactional financial management to artificial intelligence solutions that would enable them to make informed decisions, scrutinize risks, and reduce supplier costs.
Sulpari is a Seattle-based startup founded in the year 2016 by Nikesh Parekh that specializes in developing artificial intelligence solutions to allow businesses to make faster purchasing decisions using antiquated enterprise systems.
CEO of Sulpari, Nikesh Parekh, said, “We are excited for the new road ahead with Microsoft. I am ecstatic to report that 100pc of our team is continuing to work together to extend Suplari’s Spend Intelligence Cloud at Microsoft.”
He also mentioned that now the consumers can expect the company to develop better predictive and perspective insights generating platforms for the financial needs of various businesses.
Covariant, a leading AI Robotics company, raised $40 million in Series B funding in May 2020. Just after a year, the Berkeley-based AI start-up, Covariant raises $80M Series C funding, bringing its total capitalization to $147 million within two years of the company coming out from stealth mode.
The Series C round was led by returning investor, Index Ventures, with the additional participation of Radical Ventures and Amplify Partners. Covariant also added Temasek and Canada Pension Plan Investment Board (CPP Investments) as new global investors with this latest round of funding.
“With Covariant rolling out multiple applications in warehouses across Europe, North America, and Asia-Pacific over the last year, it’s the first time that AI Robotics has been successful at this scale with such variability. Covariant consistently outperforms the competition in tests by prospective clients to benchmark autonomy in real-world operations,” said Mike Volpi of Index Ventures.
Covariant was founded in 2017 by OpenAI along with researchers from the University of California, Berkeley. Last year, they deployed Covariant Brain, which was described as “universal AI that enables robots to see, reason, and act autonomously in the real world.” It’s currently active in markets across Europe, Asia, and North America. Covariant has deployed its AI and robotics technology across various sectors from groceries, Industrial supply, pharmaceuticals, parcels, general merchandise, health, beauty, and fashion.
The company currently has under 80 employees, and part of the funding will grow the team globally. Covariant has added high-profile team players to its management for meeting challenges: Ally Lynch as head of Marketing, Raghavendra Prabhu as head of Engineering and Research, and Sam Cauthen as head of People. Meanwhile, parts of the $80 million funding will continue to be invested in AI Robotics research and development (R&D) and accelerate bringing AI into the physical world.
Microchip manufacturing giant Intel launches its new free initiative ‘AI for All’ in collaboration with the Central Board of Secondary Education (CBSE) and the Ministry of Education to teach individuals the basics of artificial intelligence in India.
It is a 4 hours self-paced program that anyone can attend for free. With this new initiative, Intel plans to educate more than 1 million individuals in a span of one year.
Director of APJ and Global Partnerships and Initiatives at Intel, Shweta Khurana, said, “Artificial Intelligence has the power to drive faster economic growth, address population-scale challenges and benefit the lives and livelihoods of people. The AI For All initiative based on Intel’s AI For Citizens program aims to make India AI-ready by building awareness and appreciation of AI among everyone.”
She further mentioned that this effort of Intel shows its willingness to work with the government of India towards making the country digitally empowered and unlock the full potential of artificial intelligence in India.
The free course is divided into two sections that would talk about artificial intelligence awareness and its applications, respectively. It would also focus on resolving common misconceptions people have regarding artificial intelligence.
The COVID-19 pandemic has forced businesses to adopt a digital approach, which has accelerated the growth of artificial intelligence technologies in every industry. This course will help the citizens of India to understand the new normal in a better way and adapt accordingly.
Interested candidates can enroll themselves through the official website of CBSE.
In a recent event, the Prime Minister of India announced the launch of the AI for All initiative along with another program named ‘SAFAL.’ CBSE, in a statement, mentioned, “SAFAL will be conducted on a pilot basis in CBSE schools for students in Grades 3, 5, and 8 during the academic year 2021-22, in key curricular areas of Language, Mathematics, and EVS/Science.”
On Tuesday, DeepMind released a preprint on “Open-Ended Learning Leads to Generally Capable Agents,” outlining its initial efforts toward training an agent capable of playing a variety of games without relying on human interaction data. The team built a large training environment called XLand, that produces multiplayer mini-games within stable, “human-relatable” digital 3D scenarios on its own. This environment enables the development of new learning algorithms that dynamically control how an agent learns and the games on which it trains, allowing for the mass training of AI agents to do tasks of variable complexity.
The objective behind creating XLand is to overcome the limitations of training artificial intelligence models (and robots) using reinforcement learning. The reinforcement learning algorithms learn how to do things by synthesizing input from a huge dataset, identifying patterns, and using those patterns to generate educated guesses about fresh data. Simply put, the algorithm learns to make a series of decisions depending on the feedback it receives from its computational environment. A reinforcement learning model is rewarded for generating good predictions and punished for producing bad ones. Over time, it arrives at the optimal solution by attempting to maximize the cumulative reward.
However, the problem with this form of training is that the models are generally trained in a limited set of scenarios. Hence, if the same models are presented with a slightly different set of environments they may struggle to adapt to these environments nor produce satisfactory outcomes.
Therefore, rather than training agents to do a narrow set of activities, the DeepMind research team has discovered a universe of scenarios that may be produced procedurally. Each AI player’s aim is to maximize prizes, and each game determines the players’ unique awards. Deepmind also used population-based training (PBT) to prevent training dead ends. Population-based training is a neural network training approach that allows an experimenter to quickly select the optimum collection of hyperparameters and models for the job.
The AI agents in DeepMind’s XLand operate as a basic body in continuously changing digital surroundings that the agent views from a first-person perspective. Simple problems such as “Stand next to the purple cube,” “Bring the yellow cube onto the white corridor,” and linked conditions like “Stand next to the purple cube or in the red hallway,” were among the game tasks generated procedurally in the XLand. These tasks enable the AI agents to train themselves and generate experience by performing them.
The agents sense their surroundings by observing RGB images and receive a text description of their goal, direct feedback on success or failure follows at defined time intervals. There are other AI agents with similar or opposing aims in many of the generated games. Also, an AI agent may interact with interactive elements such as spheres, cubes, and ramps by employing tools that allow it to pick up or freeze them.
According to DeepMind, after five generations, AI agents exhibit continual breakthroughs in learning and skills that were previously unknown. In those five iterations, each AI agent has completed over 200 billion training steps due to 3.4 million distinct assignments and has played nearly 700,000 games in 4,000 different XLand environments.
The AI agents demonstrated general behavior tendencies such as exploration, like changing the state of the environment until they obtained a rewarding condition, after only 30 minutes of intense training on a new task. DeepMind reported that these agents were aware of the fundamentals of their bodies, the passage of time, and the high-level structure of the games they were playing.
Last month DeepMind claimed that reinforcement learning was enough to achieve General AI. Now it also admits that the above feat would not have been possible using the reinforcement learning method alone – thus paving way for zero-shot learning. In its blog, it wrote, “Instead of learning one game at a time, these [systems] would be able to react to completely new conditions and play a whole universe of games and tasks, including ones never seen before.”
Researchers from Skoltech Institute of Science and Technology, Lomonosov Moscow State University, and the Syntelly startup have created and trained a neural network that generates names for organic compounds using the IUPAC nomenclature system.
In their research published on Nature under Scientific Report, the team mentions creating a Transformer-based artificial neural network approach for translating between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The Struct2IUPAC converts SMILES strings to IUPAC names and IUPAC2Struct performs the reverse conversion.
IUPAC or International Union of Pure and Applied Chemistry was founded in 1919 to harmonize the chemical naming of elements and organic compounds. For instance, in the IUPAC terms, sucrose is called (2R,3R,4S,5S,6R)-2-[(2S,3S,4S, 5R)-3,4-dihydroxy-2,5-bis(hydroxymethyl)oxolan-2-yl]oxy-6-(hydroxymethyl)oxane-3,4,5-triol, and paracetamol, the active ingredient of antipyretic drugs like Tylenol, is N-(4-hydroxyphenyl)acetamide. Since the IUPAC name comprises representing organic molecules’ names in the form of chemical structures using numbers and long names, it seems inconvenient to remember. Omitting even a single digit or symbol is unacceptable in the scientific domain.
Hence we have SMILES, or Simplified Molecular Input Line Entry System, which was created to make chemical information processing easier for both humans and computers. For example, Ethanol is written as CCO, which represents the molecule’s fundamental backbone, without any hydrogens: i.e., a carbon bonded with a carbon bonded to an oxygen. The best advantage to SMILES nomenclature is that many SMILES strings can describe the same molecule. For Ethanol, OCC and C(O)C are both acceptable.
According to Skoltech research scientist Sergey Sosnin, the team initially wanted to create an IUPAC name generator for Syntelly. However, they soon realized that it would take the team more than a year to create an algorithm by digitizing the IUPAC rules. Therefore they decided to leverage their knowledge and expertise in neural network solutions instead. Sergey is also the lead author of the study and co-founder of the Syntelly startup.
The team used the standard Transformer architecture with six encoder and decoder layers and eight attention heads as the basis for their research. The encoder layer creates an encoded representation of the words in the input data (latent vector or context vector). When a latent vector is provided to the decoder, it creates a target sequence by predicting the most likely word for each time step that pairs with the input word. Also, the Transformer uses an attention mechanism that looks at an input sequence and decides at each step which other parts of the sequence are important. This helps the neural network models to selectively focus on certain parts of their input and thus reason more effectively.
The team trained the Struct2IUPAC to convert a molecule’s structural representation to an IUPAC name and IUPAC2Struct for vice versa. They used PubChem, the world’s biggest open chemical library with over 100 million organic chemicals, to serve as the basis for the new network’s training and testing. The transformer neural network Struct2IUPAC learned to convert the names with almost 98.9% accuracy (1075 mistakes per 100,000 molecules) on a subset of 100,000 random organic molecules from the test set within six weeks of designing.
In recent years, the use of neural network approaches for solving chemical issues has grown in popularity. By treating molecules and reactions like words and sentences, they have found ways to get the computer to understand the chemical compounds. Yet, despite its enormous scope, the technology is still in its infancy.
Sergey says, “We have shown that neural networks can cope with exact problems, disproving the formerly prevalent notion that they should not be used for this kind of problem. Replacing a word with a synonym is possible in machine translation, whereas a single wrong symbol results in an incorrect molecule in our task. Yet, Transformer successfully copes with this task.”
The new solution has been integrated into the Syntelly platform and is available online. The researchers anticipate that their technique will be useful for converting between chemical notations as well as other technical notation-related activities like formula synthesis and software translation.
