BHUBANESWAR: Chief minister Naveen Patnaik on Monday inaugurated Centre of Excellence on Artificial Intelligence in the College of Engineering and Technology (CET) here. This centre will conduct research on different problems of the industry and make software programmes for its solution. The college has collaborated with Tech Mahindra Limited to set up the centre and start work on different problems. The IT company and the college had signed the memorandum of understanding last month. As many as 30 students will work in the centre guided by the experts of the company.

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting requires more global inference to oversee the whole graph. To tackle this problem, we propose a dynamic intermedium attention memory network (DIAMNet) which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize different subgraph isomorphisms for the global counting. We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models. Experimental results show that learning based subgraph isomorphism counting can help reduce the time complexity with acceptable accuracy. Our DIAMNet can further improve existing representation learning models for this more global problem.

Most optimizers including stochastic gradient descent (SGD) and its adaptive gradient derivatives face the same problem where an effective learning rate during the training is vastly different. A learning rate scheduling, mostly tuned by hand, is usually employed in practice. In this paper, we propose CProp, a gradient scaling method, which acts as a second-level learning rate adapting throughout the training process based on cues from past gradient conformity. When the past gradients agree on direction, CProp keeps the original learning rate. On the contrary, if the gradients do not agree on direction, CProp scales down the gradient proportionally to its uncertainty. Since it works by scaling, it could apply to any existing optimizer extending its learning rate scheduling capability. We put CProp to a series of tests showing significant gain in training speed on both SGD and adaptive gradient method like Adam. Codes are available at https://github.com/phizaz/cprop .

In the teacher-student scenario under the assumption that the teacher's couplings are sparse and the student does not know the graphical structure, the learning curve and order parameters are assessed in the typical case using the replica and cavity methods from statistical mechanics. Our formulation is also applicable to a certain class of cost functions having locality; the standard likelihood does not belong to that class. The derived analytical formulas indicate that the perfect inference of the presence/absence of the teacher's couplings is possible in the thermodynamic limit taking the number of spins N as infinity while keeping the dataset size M proportional to N, as long as α M/N 2. Meanwhile, the formulas also show that the estimated coupling values corresponding to the truly existing ones in the teacher tend to be overestimated in the absolute value, manifesting the presence of estimation bias. These results are considered to be exact in the thermodynamic limit on locally treelike networks, such as the regular random or Erd os-R enyi graphs. Numerical simulation results fully support the theoretical predictions. Additional biases in the estimators on loopy graphs are also discussed. 1 Introduction Inference based on the classical Ising model is called the inverse Ising problem or Boltzmann machine learning, which is attracting more and more attention with the increasing interest in machine learning technologies.

Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, feeding behavior in ecological networks, and location on e-trading networks, that can be further leveraged with the network structure to attain more accurate community detection methods. We propose a novel probabilistic graphical model to detect communities by taking into account both network structure and nodes' features. The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities. Furthermore, the model is capable of determining the strength of node features and structural elements of the networks on shaping the communities. The effectiveness of the proposed approach over the state-of-the-art algorithms is revealed on synthetic and benchmark networks.

Finland is offering a hi-tech Christmas gift to all European Union citizens - a free-of-charge online course in artificial intelligence, in their own language, officials said on Tuesday. The tech-savvy Nordic nation, led by the 34-year-old Prime Minister Sanna Marin, is marking the end of its rotating presidency of the EU at the end of the year with a highly ambitious goal. Instead of handing out the usual ties and scarves to EU officials and journalists, the Finnish government has opted to give practical understanding of AI to 1 percent of all EU citizens - about five million people - through a basic online course by the end of 2021. It is teaming up with the University of Helsinki, Finland's largest and oldest academic institution, and the Finland-based tech consultancy Reaktor. Teemu Roos, a University of Helsinki associate professor in the department of computer science, described the nearly $2m project as "a civics course in AI" to help EU citizens cope with society's ever-increasing digitisation and the possibilities AI offers in the jobs market.

With the ever-changing technological environment in today's world, we have completely changed the way we interact with one another. Now, we connect via smartphones and social media. This phenomenon has grown to an extent where businesses and brands now use influencers to communicate with the public. Here at Qemploy, we have come up with a grand list of the most influential individuals or groups in the IT-consulting industry. These influencers vary between IT-specialists, IT-consultants and IT-consulting firms.

To be eligible for MSc Agriculture (Agronomy), students must have a BSc or BSc (hons) Agriculture degree. Industry focus The Institute is planning to set up an industry advisory board consisting of technology firms and traditional agro companies to help leverage the expertise of both the sectors. It is also in talks with Israel, known for its technologically advanced agro industry, to formalise possible collaborations. Job prospects "Since smart agriculture is still at its infancy in India, the onus is on us to convince potential recruiters about the technology-enabled programmes and students' job-readiness. However, food processing, which will be one of the focus areas of the BTech Food Technology programme, is emerging as a sunrise industry and is one of the 25 focus sectors of the government's'Make in India' initiative," Jha says. Agri-based government jobs and food officers looking into food safety, are some of the possible job profiles for this sector. "With agritech having received 300% more funding in 2019 as compared to 2018 according to data released by NASSCOM survey, technology in agriculture is emerging as an area with abundance of opportunities for young entrepreneurs," Jha adds.

Many employers are using internships, boot camps and other after-school programs to net promising candidates before they earn their degrees, chief information officers say. "We regularly engage with students to help build brand awareness and candidate pipelines," said Ashley Pettit, CIO at State Farm Mutual Automobile Insurance Co. The Bloomington, Ill., insurer has about 6,000 technology workers, she said, and a recent online career fair it organized attracted more than 3,000 job seekers. Ms. Pettit was one of 30 information-technology executives who responded via email to CIO Journal's annual end-of-year questionnaire about hiring and other issues. CompTIA, an IT trade group, estimates that--despite ebbs and flows in the job market--the number of U.S. tech jobs is expected to grow 13.1% by 2026 from 2016, compared with 10.7% for all occupations.

The irony of the ethical scandal enveloping Joichi Ito, the former director of the MIT Media Lab, is that he used to lead academic initiatives on ethics. After the revelation of his financial ties to Jeffrey Epstein, the financier charged with sex trafficking underage girls as young as 14, Ito resigned from multiple roles at MIT, a visiting professorship at Harvard Law School, and the boards of the John D. and Catherine T. MacArthur Foundation, the John S. and James L. Knight Foundation, and the New York Times Company. Many spectators are puzzled by Ito's influential role as an ethicist of artificial intelligence. Indeed, his initiatives were crucial in establishing the discourse of "ethical AI" that is now ubiquitous in academia and in the mainstream press. In 2016, then-President Barack Obama described him as an "expert" on AI and ethics. Since 2017, Ito financed many projects through the $27 million Ethics and Governance of AI Fund, an initiative anchored by the MIT Media Lab and the Berkman Klein Center for Internet and Society at Harvard University.