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) …
Clayton Christensen, author of The Innovator's Dilemma and a Harvard Business School professor, died last month. Normally, this word suggests a negative connotation. The talkative student was disruptive to the rest of the class, or the reckless driver was disruptive to the flow of traffic. Dictionary.com lists the following words as related to disruptive: upsetting, disturbing, troublesome, rowdy, troublemaking. Yet this word is used routinely in society and business, often with a positive, or at least neutral, connotation.
The past few years have witnessed breakthroughs in reinforcement learning (RL). From the first successful use of RL by a deep learning model for learning a policy from pixel input in 2013 to the OpenAI Dexterity program in 2019, we live in an exciting moment in RL research. Consequently, we need, as RL researchers, to create more and more complex environments and Unity helps us to do that. Unity ML-Agents toolkit is a new plugin based on the game engine Unity that allows us to use the Unity Game Engine as an environment builder to train agents. From playing football, learning to walk, to jump big walls, to train a cute doggy to catch sticks, Unity ML-Agents Toolkit provides a ton of amazing pre-made environment.
This post is part of the "superblog" that is the collective work of the participants of the GAN workshop organized by Aggregate Intellect. This post serves as a proof of work, and covers some of the concepts covered in the workshop in addition to advanced concepts pursued by the participants. The original GAN (Goodfellow, 2014) (https://arxiv.org/abs/1406.2661) is a generative model, where a neural-network is trained to generate realistic images from random noisy input data. GANs generate predicted data by exploiting a competition between two neural networks, a generator (G) and a discriminator (D), where both networks are engaged in prediction tasks. G generates "fake" images from the input data, and D compares the predicted data (output from G) to the real data with results fed back to G. The cyclical loop between G and D is repeated several times to minimize the difference between predicted and ground truth data sets and improve the performance of G, i.e., D is used to improve the performance of G.
Is the success of Google that of the algorithms or that of data? Today's fascination with artificial intelligence (AI) reflects both our appetite for data and our excitement about the new opportunities in machine learning. Here, I argue that newcomers to the field of data science are blinded by the shiny object of magical algorithms -- and that they forget the critical infrastructures that are needed to create and to manage data in the first place. There are now many companies that provide AI services. An attractive offer should affirm all of the above -- the sole expertise in analyses and algorithms is generally insufficient, as it does not necessarily address the data part of the equation.
The main pillar of the plan is focused on accelerating Mexico's digital transformation through democratizing the access to technology. The company announced plans to establish a new cloud datacenter region in Mexico to deliver its intelligent and trusted cloud services to serve Mexico's public entities, organizations and Mexican society, including Microsoft Azure, Office 365, Dynamics 365 and the Power Platform. This datacenter region is an important part of Microsoft's $1.1 billion investment plan in Mexico over the next five years. The plan also includes a robust education and skilling program with different initiatives the first one being the creation of three laboratories and a virtual classroom, in collaboration with public universities to create an education platform for digital skills, to expand employability in future generations. The first initiative of the commitment to apply artificial intelligence to create societal impact is an investment in the project "Artificial Intelligence to Monitor Pelagic Sharks in the Mexican Pacific Ocean" (Shark ID), focused on the conservation of Mako shark species, driven by Mexico Azul, as part of the initiative AI for Earth, creating societal impact.
Six European cities – Helsinki, Amsterdam, Copenhagen, Paris, Stavanger, and Tallinn – join forces in a new project named AI4Cities. The project challenges enterprises, researchers and others to develop solutions utilising artificial intelligence (AI) to generate cuts to carbon dioxide emissions, said the City of Helsinki in a press release. Helsinki emphasises utilisation of data and AI in its digitalisation programme to achieve the city's climate goals. The participating cities' respective programmes to cut carbon dioxide emissions emphasise emissions from transport and housing. Consequently, the AI4Cities Project focuses on emissions generated from transport and traffic as well as the energy consumption by buildings.
If an employee who recently gave two weeks' notice starts downloading large numbers of files from the company network and copying them to a thumb drive, it is entirely possible that he or she has no malicious intent. The employee could be saving innocuous files related to their employment record or examples of marketing pieces they created. However, in a small number of cases, the employee could be attempting to take confidential product designs, sensitive legal information, private employee data or trade secrets with them to a rival company. It can be difficult for a company to even spot these "insider risks," much less distinguish between routine behavior and the outlier that could destroy a company's competitive advantage or reputation. That's why Microsoft is offering a new Insider Risk Management solution within Microsoft 365 that uses machine learning to intelligently detect potentially risky behavior within a company.
The Artificial Intelligence (AI) sector is rapidly growing with algorithms developing to meet and even exceed human capabilities. One awesome example is Deep Learning (DL), and emerging machine learning subfield which can continue to evolve on its own, without the need for continued programming. When companies want to use AI to expand and to get their startup to take off, one aspect is essential: the technology with which they choose to operate must be combined with an appropriate deep learning framework, particularly since each framework serves a specific purpose. In terms of smooth and quick business development, as well as efficient delivery, finding the perfect fit is not only important but also necessary. Given that deep learning is the key to performing tasks of a higher level of complexity and logical thinking, successfully building and deploying them proves to be quite a difficult challenge for data scientists and data engineers worldwide.
Every linguist has probably at some time had a conversation similar to the following. Somebody asks them: "so, what do you do for a living?" and they answer "I'm a linguist" without an extended explanation. Then a somewhat puzzled look appears in the face of the person who asked. Right, linguists deal with languages, so the next question goes something like this: "so do you speak a lot of languages?". While this comes in handy and is many times the case, it's not always true (ever heard of Chomsky?) because the focus of linguists is language as a system and as a human ability, rather than specific languages such as Japanese, French or Xhosa.
Sign in to report inappropriate content. Shaun Moore is the founder and CEO of Trueface, a facial recognition company working to make computers see like humans. Trueface is involved with many companies and most recently has been helping the US Air Force increase its base security. In the podcast we talk about how facial recognition should be used, the ethics in its application, and the consequences of countries like China using the technology.