Every day, our kids are swept through the world by algorithms. YouTube algorithms decide what videos they watch, GPS algorithms map what route they take to school, Spotify algorithms select what songs they hear, and personal assistants like Siri and Alexa advise them -- all of it driven by artificial intelligence. Kids (and adults!) leave these passive engagements with AI without any physical product -- just an endless stream of passive consumption. Instead of getting carried away by these digital currents, teachers, parents, and caregivers should show kids how to experiment with powerful tools like machine learning and neural networks. We must raise kids who are capable of working alongside artificial intelligence in the workplace.

New Delhi: When it comes to programming languages in India, Python is most popular among the students for its role in Artificial Intelligence (AI) applications, data science, Machine Learning (ML) and data analytics, US-based online education company Coursera has said. Python dominated the top 10 list with courses like'Programming for Everybody', 'Python Data Structures', 'Python for Data Science and AI' and more. Python is also easy to get started with, offers a lot of flexibility and is versatile. "Its open source nature makes it easy to learn. A large number libraries for tasks like web development, text processing, calculations add to its appeal," the repor said.

When people learn mathematical patterns or sequences, they are able to identify the concepts (or rules) underlying those patterns. Having learned the underlying concepts, humans are also able to generalize those concepts to other numbers, so far as to even identify previously unseen combinations of those rules. Current state-of-the art RNN architectures like LSTMs perform well in predicting successive elements of sequential data, but require vast amounts of training examples. Even with extensive data, these models struggle to generalize concepts. From our behavioral study, we also found that humans are able to disregard noise and identify the underlying rules generating the corrupted sequences. We therefore propose a Bayesian model that captures these human-like learning capabilities to predict next number in a given sequence, better than traditional LSTMs.

In this work, we present an alternative approach to making an agent compositional through the use of a diagnostic classifier. Because of the need for explainable agents in automated decision processes, we attempt to interpret the latent space from an RL agent to identify its current objective in a complex language instruction. Results show that the classification process causes changes in the hidden states which makes them more easily interpretable, but also causes a shift in zero-shot performance to novel instructions. Lastly, we limit the supervisory signal on the classification, and observe a similar but less notable effect.

Relation Extraction (RE), the task of detecting and characterizing semantic relations between entities in text, has gained much importance in the last two decades, mainly in the biomedical domain. Many papers have been published on Relation Extraction using supervised machine learning techniques. Most of these techniques rely on statistical methods, such as feature-based and tree-kernels-based methods. Such statistical learning techniques are usually based on a propositional hypothesis space for representing examples, i.e., they employ an attribute-value representation of features. This kind of representation has some drawbacks, particularly in the extraction of complex relations which demand more contextual information about the involving instances, i.e., it is not able to effectively capture structural information from parse trees without loss of information. In this work, we present OntoILPER, a logic-based relational learning approach to Relation Extraction that uses Inductive Logic Programming for generating extraction models in the form of symbolic extraction rules. OntoILPER takes profit of a rich relational representation of examples, which can alleviate the aforementioned drawbacks. The proposed relational approach seems to be more suitable for Relation Extraction than statistical ones for several reasons that we argue. Moreover, OntoILPER uses a domain ontology that guides the background knowledge generation process and is used for storing the extracted relation instances. The induced extraction rules were evaluated on three protein-protein interaction datasets from the biomedical domain. The performance of OntoILPER extraction models was compared with other state-of-the-art RE systems. The encouraging results seem to demonstrate the effectiveness of the proposed solution.

Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at https://dice.mpi-inf.mpg.de.

This means that the test favors "program synthesis," the subfield of AI that involves generating programs that satisfy high-level specifications. This approach is in contrast with current trends in AI, which are inclined toward creating programs that are optimized for a limited set of tasks (e.g., playing a single game). In his experiments with ARC, Chollet has found that humans can fully solve ARC tests.

New Delhi: When it comes to programming languages in India, Python is most popular among the students for its role in Artificial Intelligence (AI) applications, data science, Machine Learning (ML) and data analytics, US-based online education company Coursera has said. Python dominated the top 10 list with courses like'Programming for Everybody', 'Python Data Structures', 'Python for Data Science and AI' and more. Python is also easy to get started with, offers a lot of flexibility and is versatile. "Its open-source nature makes it easy to learn. A large number libraries for tasks like web development, text processing, calculations add to its appeal," the report said.

Chennai: To reverse the droopy admission trend of engineering courses, colleges in Tamil Nadu have turned their eyes towards emerging areas such as artificial intelligence, data science and machine learning. More than 35 engineering colleges have applied to Anna University expressing interest to start BTech courses in artificial intelligence and data science, and computer science and business systems for the next academic year. All India Council for Technical Education (AICTE) has announced that engineering colleges would be allowed to start new courses in artificial intelligence, data science, cyber security, machine learning and block chain. Anna University had invited application for starting a course in artificial intelligence and data science. It has also began to frame syllabus for the course.

Furthermore, in those two years, the average annual growth rate of investments in robotics was an impressive 57%. What was most troublesome is that this rise mostly impacted workers with the lowest levels of education. In those same two years, robots replaced 9.4% of employees with a junior high school degree or below. This was in direct contrast to the fact that demand for college degree workers grew by 3.6%. According to the International Federation of Robotics, China has become a global leader in automation.