If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
BENGALURU: As India plans to roll out a nationwide facial recognition system this year, Infosys co-founder Kris Gopalakrishnan believes that the country must develop its own databases for efficient implementation of breakthrough technologies that use artificial intelligence and machine learning. A facial recognition system is a technology capable of identifying or verifying a person by analysing patterns based on the person's facial textures and shape. Gopalakrishnan noted that India should carry out its own trials before implementing the facial recognition systems, as currently the algorithms used to train these mostly employ data of white men belonging to the Anglo-Saxon community, and it is unclear whether it will work properly in the country. "We also need to look at biases. One of the reasons why I believe India must do research in artificial intelligence (AI) and machine learning (ML) particularly is because most of the databases that are used to train these systems which we use today are being trained with data which is not from India," he told PTI in an interview on the sidelines of the Infosys Prize ceremony here.
Innovative start-ups can play a major role in the Indian healthcare system as the fourth industrial revolution is characterised by a fusion of technologies that is blurring the lines between the physical, digital and biological domains, according to Kris Gopalakrishnan, Chairman, Axilor Ventures Private Ltd, Bengaluru. Delivering the 27th convocation address of Manipal Academy of Higher Education (MAHE) in Manipal on Friday, Gopalakrishnan, said that there is tremendous disruption at the edge and at the intersection of emerging technology domains and economic activity. Stating that Artificial Intelligence (AI) is becoming one of the most important technologies of all time, he said AI is now getting deeper into what were so far specialist human domains. Referring to the example of a real-time image-guided and robot-assisted surgery where imaging coupled with robotic assistance helps in assessing the area of procedure, monitoring the tools in 3D, and updating patho-physiology knowledge of the targeted tissue in real-time, Gopalakrishnan said this innovation is at the intersection of AI, robotics, biotechnology, telecommunications and clinical domains. Many more such innovations are emerging and defining the 21st Century, he said.
With new technologies disrupting businesses and changing the rules of engagement, India faces a daunting task to reskill its huge workforce for Artificial Intelligence (AI), Infosys co-founder Kris Gopalakrishnan says. "India has a major challenge of transitioning its young workforce to the fourth industrial revolution called AI after the eras of agriculture, manufacturing and services," Gopalakrishnan said in an interview. Gopalakrishnan, 63, well-known as'Kris', is one of the seven co-founders of the iconic IT firm, who became its chief executive after fellow co-founder Nandan Nilekani quit in mid-2009 to set up the Unique Identification Authority of India (UIDAI) for issuing Aadhaar cards to over a billion citizens. "As the large workforce is engaged in diverse occupations such as agriculture, manufacturing and white-collar jobs in the services sector, it needs to be re-skilled to sustain the jobs, as AI will replace traditional jobs," said Gopalakrishnan. Originating in the mid-1950s as an academic discipline, AI involves machines emulating human intelligence.
The field of deep learning is still in flux, but some things have started to settle out. In particular, experts recognize that neural nets can get a lot of computation done with little energy if a chip approximates an answer using low-precision math. But some tasks, especially training a neural net to do something, still need precision. IBM recently revealed its newest solution, still a prototype, at the IEEE VLSI Symposia: a chip that does both equally well. The disconnect between the needs of training a neural net and having that net execute its function, called inference, has been one of the big challenges for those designing chips that accelerate AI functions.
With Elon Musk and Mark Zuckerberg sparring over its ethics and China announcing its intention to create a $150 billion domestic industry based on it, Artificial Intelligence is perhaps the most discussed topic in the tech news cycle. It's likely to be a talking point no matter what your favourite watering hole for tech news. Billions of dollars have been invested by VCs in AI since 2016 with the US and China leading the race in record funding in terms of deals and dollars. In sharp contrast, Indian startups have collectively raised less than $100 million from (2014-2017YTD), according to data from startup analytics firm Tracxn -- that's smaller than Andrew Ng's recently launched $150 million VC fund. Another way to look at it: Grammarly, a Valley-based spell check tool raised more dollars than all of India's AI startups put together in the past three and a half years.
Smartwatches that track the health of patients and send out an alarm if they fall down, anti-loss alarm chip, and apps that convert mobile phones into a television remote were just some of the innovative technologies on display at BengaluruITE.Biz 2016, a flagship event of Karnataka government. Hundreds of startups and technology companies pitched their products and services to an audience of top business executives, government officials, and investors at the event held in Palace Grounds here. Industry stalwarts said they had come together on a single platform because information technology was changing significantly and they needed to equip themselves with future technologies to stay relevant. Kris Gopalakrishnan, co-founder of Infosys, India's second-largest software exporter, said these significant changes are creating new opportunities such as autonomous vehicles and telemedicine. "We require new tools and techniques to create and manage new knowledge," said Mr. Gopalakrishnan.
With the increase in adoption of Electric Vehicles (EVs), proper utilization of the charging infrastructure is an emerging challenge for service providers. Overstaying of an EV after a charging event is a key contributor to low utilization. Since overstaying is easily detectable by monitoring the power drawn from the charger, managing this problem primarily involves designing an appropriate "penalty" during the overstaying period. Higher penalties do discourage overstaying; however, due to uncertainty in parking duration, less people would find such penalties acceptable, leading to decreased utilization (and revenue). To analyze this central trade-off, we develop a novel framework that integrates models for realistic user behavior into queueing dynamics to locate the optimal penalty from the points of view of utilization and revenue, for different values of the external charging demand. Next, when the model parameters are unknown, we show how an online learning algorithm, such as UCB, can be adapted to learn the optimal penalty. Our experimental validation, based on charging data from London, shows that an appropriate penalty can increase both utilization and revenue while significantly reducing overstaying.
Effective placement of charging stations plays a key role in Electric Vehicle (EV) adoption. In the placement problem, given a set of candidate sites, an optimal subset needs to be selected with respect to the concerns of both (a) the charging station service provider, such as the demand at the candidate sites and the budget for deployment, and (b) the EV user, such as charging station reachability and short waiting times at the station. This work addresses these concerns, making the following three novel contributions: (i) a supervised multi-view learning framework using Canonical Correlation Analysis (CCA) for demand prediction at candidate sites, using multiple datasets such as points of interest information, traffic density, and the historical usage at existing charging stations; (ii) a mixed-packing-and- covering optimization framework that models competing concerns of the service provider and EV users; (iii) an iterative heuristic to solve these problems by alternately invoking knapsack and set cover algorithms. The performance of the demand prediction model and the placement optimization heuristic are evaluated using real world data.
If we were to try to represent the crystallization problem as an AI planning problem, it would have to be cast as a reactive planning problem, as the environment is dynamically changing. The biggest challenge would arise in how to (re)plan based on evaluation partial results. Thus, we would need an evaluation function that can assign probabilities to different regions of the search space of experiments based on observation and evaluation of partial results over a period of time. These factors make this problem very different from traditional AI planning problems(Russell Norvig 1995). Key Ideas The main ideas that are represented in the framework in Figure 4 can be stated as follows: 1. Economic variables are important in strategic decision making, and hence must be included as part of every data gathering and data analysis project.