We live in the midst of the invisible revolution: artificial intelligence, simply defined as intelligence in machines, is all around us, embedded in our everyday apps and devices. AI technology is not only amplifying human ingenuity, but opening more and different kinds of opportunities in the tech industry, and according to a study by the job-matching service The Ladders, science, technology, engineering and math jobs are growing the fastest. To make AI available for all and encourage more Latinx to pursue a tech-related job, Microsoft is offering several training courses on AI designed for a variety of expertise levels. Here are three free training in AI courses that you can take advantage of.
Microsoft Professional Program for AI: This program provides job-ready skills and real-world experiences to aspiring AI engineers and those looking to improve their skills in AI. The series of online courses feature hands-on labs and expert instructors, and takes students from basic introduction of AI to mastery of the skills needed to build models for AI solutions that exhibit human-like behavior and intelligence. Each course runs for three months and starts at the beginning of each quarter. Microsoft AI Residency Program: This program offers students the opportunity to work alongside prominent Microsoft researchers and engineers to gain real-world, hands-on experience industry.
Participants can learn for a year about deploying machine learning solutions across a range of areas such as healthcare, entertainment and productivity. AI School: This training provides online videos and other assets to help developers build AI skills, including general educational tools to expand AI capabilities and specific guidance on the use of Microsoft's tools and services. With the available resources, you can learn more about Bot Framework, Azure Machine Learning, Cognitive Toolkit, and much more. Whether your solutions are existing or new, this is the intelligence platform to build on. The future is now, and at Microsoft we believe everyone should have the resources to learn more and become an expert in AI. Tags: Accessibility, Artificial Intelligence, cloud, data, Education, Machine Learning, Microsoft Latinx, The Future is now.
There can be no two opinions as to what a highbrow is. He is the man or woman of thoroughbred intelligence who rides his mind at a gallop across country in pursuit of an idea. author: Virginia Woolf
Why AI Is The Most Dangerous Thing You Can Imagine
In the world of business, science and technology, is much talk of these three concepts, but often do not have a clear idea of their meaning, their relationships and their limits. In this post we will define the concept of Machine Learning and its differences with respect to the concepts of Artificial Intelligence and Deep Learning. For example, a chess machine detects the movements of the chips on the Board, and applying the rules of chess to the data they have collected, decide the best move.
The digital assistants like Siri and Cortana are also examples of this type of IA. You can give us the weather forecast, or recommend an alternative route to go to work, but not to read our messages and delete that are not important to us. One of his best-known areas of application is the robotic, but it has important applications in the fields of medicine, education, entertainment, information management, mathematics, military applications, urban design, architecture etc. Machine Learning or machine learning is a branch of Artificial intelligence that began to gain importance from the 80s. It is a form of AI that no longer depends on rules and a programmer, but that computer can establish their own rules and learning itself.
One of the algorithms of ML that most arouses expectation, are neural networks, a technique inspired by the functioning of neurons in our brain. There is no single definition of what is Deep Learning. In general, when we talk about Deep Learning we are talking about a class of Machine Learning algorithms based on neural networks which, as we have seen, characterized by cascading data processing. The input signal spreads by the different layers, and each of them is subject to a non-linear transformation that is extracting and transforming the variables according to certain parameters. There is a limit to the number of layers that must have a neural network to be considered Deep Learning.
Team leaders were responsible for monitoring these tickets determining what was holding up completion of the ticket, and, if needed, bringing in additional resources to speed up the closure of the ticket. My team leader wasn't excited with this approach, believing that calling people on the phone was better, but the e-mails helped me clear a good percentage of tickets and cut down on the overall time I needed to do the job. As the task force continued its work, I wrote a program that accessed the ticket system database and automatically generated a report of all past-due tickets and emailed the report to the responsible parties. As AI is implemented over the coming years, it could potentially replace many existing jobs. In the past when jobs were replaced by automation or to new technology, other jobs always sprang up to take their place.
In the 1970s, for example, it was thought that the advent of ATMs would eliminate many bank teller jobs and increase back-office technology jobs. When many bank teller jobs were eliminated at individual bank locations, it became cheaper to run a single location, so the number of locations increased, which drove up the total number of bank tellers and created new technology jobs overall. As individual banks reduced traditional teller positions, new positions were created that required hybrid more marketing, customer relationship, problem-solving, and sales management skills than the jobs that they replaced. In accounting, the advent of AI and other technologies also threatens to reduce the number of jobs in the profession.
Like the bank teller jobs of the 1970s, accountants with advanced business, relationship management, and technology skills will experience an increased demand. The challenge is that advances in technology are evolving much faster now, so each of us needs to continually anticipate the automation of components of our jobs and evaluate our career paths to see where we're vulnerable. No, it isn't easy keeping a job in the 21st Century, but it can be rewarding if we're aware of the trends and continue learning to stay ahead of them.
Why AI Is The Most Dangerous Thing You Can Imagine
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The original theme song to the Transformers was actually "Chuck Norris–more than meets the eye, Chuck Norris–robot in disguise," and starred Chuck Norris as a Texas Ranger who defended the earth from drug-dealing Decepticons and could turn into a pick-up. This was far too much awesome for a single show, however, so it was divided.
In 1950, computer scientist and philosopher Alan Turing published a paper on ‘Computing machinery and intelligence’ that is often referred to as the origin of modern artificial intelligence. In it, he described the capacity for computers of the future to to display human-like capacities such as reasoning, learning, planning and creativity.
Artificial intelligence as a field began developing in the 1960s around the idea that it should be possible to deconstruct intelligent human behaviors as a succession of logical rules, transcribed in algorithms, which machines could follow to display intelligent behavior. As learning is one of the key features of human intelligence, scientists derived ways to train the computers to become so familiar with certain topics that they could identify the key components of those topics automatically. For example, if the objective is to teach a machine to recognize pictures with cats, the computer is fed with thousands of pictures, including pictures with cats. The learning capacity of these machines is based on the ability of the algorithm to find statistical correlations in the data it analyses, that is to say, interdependence of variables in the data – or in layman’s terms, finding the cat in the haystack.
By applying the methodologies of how humans learn to identify object patterns, artificial intelligence leverages a computer’s ability to analyze huge quantities of data to find statistical correlations, in essence to deploy human learning methods at scale. Machine logic does differ from human thought pattern, and a fundamental aspect of machine learning techniques is that there is no way to know how it makes its decision on a given task. In computer logic, the associations necessary to fulfill the given task are left to the computer itself to define. Recently, when Facebook AI researchers enabled bot to bot discussion, they had to shut down the experiment as the bots had developed their own “shorthand” in communication, attributable to the computer’s desire to make communication more efficient.
Today, artificial Intelligence can be used to write stories or create artworks such as paintings or musical compositions, as well as analyze large sets of data. Advances in AI have established two distinct types of machine learning, called “Narrow” and “Strong”. Data-driven AI is referred to as ‘narrow AI’ or ‘ weak AI’ because it creates machines that are only able to do one task very well: recognize cats; go play; invent a recipe. Narrow artificial intelligence systems lack common sense and intentionality, and it is still not possible for machines to understand what would come next in a series of images or to understand the broader context of a scene in a given image.
Strong artificial intelligence, on the other hand, harks back to the original AI quest to create machines that are able to display the same level of intelligence as humans. Often referred to as artificial general intelligence or ‘strong AI’, this type of machine learning would perform different tasks, show common sense and share intentionality. Outstripping human intelligence, Strong AI would lead to a technological singularity’, leaving humans in the hands of machines. And that is where artificial intelligence gets a lot more real.
Watching the recent Supreme Court television coverage and the wide range of emotions shown by all parties involved gave me pause to wonder whether artificial intelligence could be effectively deployed to analyze emotional response in humans.
While studying emotions is important for a number of reasons, artificial intelligence may not be able to cope:
Emotions are the most unpredictable aspect of a person, but also a common aspect of the human condition;
An emotion shows the way that a person perceives the world, and an accurate categorization of emotion could be a better indicator of truthfulness or deception;
Emotions are an important aspect of human intelligence and play a significant role in human decision-making processes.
However, there is little information about what emotions really are, and the boundaries to the domain of what experts have called emotion are so blurry that it sometimes appears that everything is an emotion.
While emotions are a common aspect of the human condition, it should be evident that waiting for a consensus on how experts actually categorize emotions is unrealistic because the study of emotions is still incomplete. There is some acceptance of classifying emotions according to their physical manifestation, i.e. humans show emotion on their faces and in their body movement, and those cues could help classify and categorize emotional response. But each of us is different, and it is perhaps our pre-conditioning that dictates not only the emotional response but also the level of that response. Someone who was taught to foster joy may react differently than someone who is serially depressed when exposed to the same stimulus – hence the notion that emotions are unpredictable.
A main problem for categorizing emotions stems from language, because there are some emotion words that have different meanings in different countries, kind of like how Eskimos have hundreds of words for the condition of snow, but most folks just call it “snow”. Nevertheless, structuring emotions to be interpreted by AI means finding a common ground for the emotion words, even if the categories and classifications are different in different cultures. This may be impossible to codify, and certainly would be reductive as emotional responses and triggers evolve over time and with human experience. At one time, cars frightened people, and while today some people’s ability to not use a turn signal may be frightening, the aspect of driving a car or seeing a car does not strike the kind of fear in humans it once did.
Motoring is one of the most contemptible soul-destroying and devitalizing pursuits that the ill-fortune of misguided humanity has ever imposed upon its credulity…[they are] a pack of fiends released from the nethermost pit. – C. E. M. Joad, circa 1900
In addition, the notion of “mixed emotions” about a topic are inherent in humans as well, which may defy categorization – like the old joke about someone seeing their Mother In Law drive off a cliff in their new sport scar. One of the theories put forth is commonly known as the Strongest Emotion Model, which seeks to address this mixed emotions dilemma by assigning values for each emotion felt, and tagging the one that has the highest value as the predominant, and ultimately motivating emotional response.
While facial expressions can also be tied into mixed emotional response, they may not agree with the verbal cues offered as support of underlying emotion. If you watch the SCOTUS court proceedings, there are often facial expressions that seem to run counter to what is being said, and those facial expressions can be viewed as a more accurate representation of underlying emotion – like telling someone their disgusting dinner dish is tasty while wrinkling the nose, or when during a poker game a great hand and the exhilaration of the win is masked by a poker face. It gets deeper, as some emotions have no expression whatsoever, and that there are some others that have the same nuances across emotion, and thus, it is impossible to differentiate between them.
The goal of using artificial intelligence to analyze emotions is a noble direction in which to take machine learning. However, as humans offer such a wide variable to study, I would argue that a concrete basis on which to formulate artificial intelligence for the accurate assessment of the dynamics of human emotion is too lofty a goal for current technology.
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Startup Weekend is the world largest program for entrepreneurs, where in 54 hours people from all areas of the technology industry come together to create a company.
But there are people behind the scenes that make it run successfully, and we can't thank them enough!
In Charleston, two wonderful individuals have been spearheading the event: Stacey Segal and Rich Conte
Stacey Segal is the founder of BrightVine Solutions, a team of technology consultants who work to update and deploy cutting edge solutions into the nonprofit industry. Often, nonprofits don't have the capital to consistently update their technology needs and meet the demands of their volunteers, donors, and those they serve. BrightVine focuses on the deployment of Blackbaud software, extensive training, and hands-on service to make sure that nonprofits have the tools they need at a price they can afford to fulfill their mission in the modern age
Rich Conte is a veteran Technology leader with 20+ years of experience in Product Management, User Experience and Product Development. He has played significant roles in the development and launch of Enterprise-class B2C CRM and Investment Performance solutions at Blackbaud and Thomson Financial. Rich is also the Executive Producer and Host of the Tech Life Podcast and has built an audience of 25k listeners per month by sharing the stories of entrepreneurs and others in and around the Charleston SC Technology community.