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WELCOME TO ESPER AI

We are an AI and Data Science company

Our mission is to make AI work for your organisation through the use of, cutting-edge technology, domain expertise and in-depth experience.

 

AI KNOWLEDGE MAP

"AI Knowledge Map" termed by Francesco Corea

  • Logic-based tools: tools that are used for knowledge representation and problem-solving.

  • Knowledge-based tools: tools based on ontologies and huge databases of notions, information, and rules.

  • Probabilistic methods: tools that allow agents to act in incomplete information scenarios.

  • Machine learning: tools that allow computers to learn from data.

  • Embodied intelligence: engineering toolbox, which assumes that a body (or at least a partial set of functions such as movement, perception, interaction, and visualization) is required for higher intelligence.

  • Search and optimization: tools that allow intelligently searching through many possible solutions.

 
 

TECHNOLOGY

COMPUTER VISION (CV)

Methods to acquire and make sense of digital images (usually divided into activities recognition, images recognition, and machine vision).

NATURAL LANGUAGE PROCESSING (NLP)

Sub-field that handles natural language data (three main blocks belong to this field, i.e., language understanding, language generation, and machine translation).

NEURAL NETWORKS (NNS OR ANNS)

A class of algorithms loosely modeled after the neuronal structure of the human/animal brain that improves its performance without being explicitly instructed on how to do so. The two majors and well-known sub-classes of NNs are Deep Learning (a neural net with multiple layers) and Generative Adversarial Networks (GANs — two networks that train each other).

DISTRIBUTED ARTIFICIAL INTELLIGENCE (DAI)

A class of technologies that solve problems by distributing them to autonomous “agents” that interact with each other. Multi-agent systems (MAS), Agent-based modeling (ABM), and Swarm Intelligence are three useful specifications of this subset, where collective behaviors emerge from the interaction of decentralized self-organized agents.

AFFECTIVE COMPUTING

A sub-field that deal with emotions recognition, interpretation, and simulation.

DECISION NETWORKS

A generalization of the most well-known Bayesian networks/inference, which represent a set of variables and their probabilistic relationships through a map (also called directed acyclic graph).

 

TRENDING

MAKE NO MISTAKE: AI IS THE NEW GLOBAL BENCHMARK

A World Economic Forum article, “AI looks set to disrupt the established world order. Here’s how” is based on a report by Tortoise Intelligence that notes how China’s centralized approach to research on artificial intelligence is much more efficient than its traditional competitor, the United States, which opts for research initiatives distributed among many private competitors linked to some public initiatives or enhanced with public money.Intelligence Make no mistake: AI is the new global benchmark A World Economic Forum article, “AI looks set

INTEL EXAMINES WHETHER AI CAN RECOGNISE FACES USING THERMAL IMAGING

Researchers from Intel have published a study examining whether AI can recognise people’s faces using thermal imaging. Thermal imaging is often used to protect privacy because it obscures personally identifying details such as eye colour. In some places, like medical facilities, it’s often compulsory to use images which obscure such details. AI is opening up many new possibilities so Intel’s researchers set out to determine whether thermal imaging still offers a high degree of privacy.

COMPUTER-BASED WEATHER FORECAST: NEW ALGORITHM OUTPERFORMS MAINFRAME COMPUTER SYSTEMS

The exponential growth in computer processing power seen over the past 60 years may soon come to a halt. Complex systems such as those used in weather forecast, for example, require high computing capacities, but the costs for running supercomputers to process large quantities of data can become a limiting factor. Researchers have recently unveiled an algorithm that can solve complex problems with remarkable facility -- even on a personal computer.

BRIDGING THE GAP BETWEEN HUMAN AND MACHINE VISION

Researchers develop a more robust machine-vision architecture by studying how human vision responds to changing viewpoints of objects. Suppose you look briefly from a few feet away at a person you have never met before. Step back a few paces and look again. Will you be able to recognize her face? “Yes, of course,” you probably are thinking. If this is true, it would mean that our visual system, having seen a single image of an object such as a specific face, recognizes it robustly despite changes to the object’s position and scale, for example.