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) …
The advent of robotics has been a boon for the industrial sector and new innovations have been offering improved efficiency, less downtime, and better products. From implementing robots for mundane tasks to enhancing precision of existing tasks, industrial robotics has come a long way. The advancements in technologies have enabled manufacturers to utilize robots that are lightweight, perform multiple tasks, and improve efficiency. Moreover, they have been aiming for safety, reliability, and improved environment for workers. Emerging technologies such as artificial intelligence, machine learning, and others have been incorporated in robots for mimicking human intelligence and facilitating some of the tasks of humans.
IDC predicts spending on AI systems will reach $97.9B in 2023, more than two and one-half times the ... [ ] $37.5B that will be spent in 2019. Machine learning's growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems quickly. Open jobs requiring TensorFlow experience is a useful way to quantify how prevalent machine learning is becoming in business today. There are 4,134 open positions in the U.S. on LinkedIn that require TensorFlow expertise and 12,172 open positions worldwide as of today. Open jobs on LinkedIn requesting machine learning expertise in the U.S. further reflect its growing dominance in all businesses.
A synapse is the connection between nodes, or neurons, in an artificial neural network (ANN). Similar to biological brains, the connection is controlled by the strength or amplitude of a connection between both nodes, also called the synaptic weight. Multiple synapses can connect the same neurons, with each synapse having a different level of influence (trigger) on whether that neuron is "fired" and activates the next neuron. In ANNs, each neuron is defined through its input and its activation function, and its outputs. A synapse is often referred to as a node in the machine learning terminology.
Computer vision based melanoma diagnosis has been a side project of mine on and off for almost 2 years now, so I plan on making this the first of a short series of posts on the topic. This post is intended as a quick/informative read for those with basic machine learning experience looking for an introduction to the ISIC problem, and those just getting out of their first or second machine learning/data mining course who'd like a simple problem to get their hands dirty with. Tools for early diagnosis of different diseases are a major reason machine learning has a lot of people excited today. The process for these innovations is a long one: Labeled datasets need built, engineers and data scientists need trained, and each problem comes with its own set of edge cases that often make building robust classifiers very tricky (even for the experts). Here I'm going to focus on building a classifier.
Army researchers have developed a new approach for training machine learning models that can better withstand dirty and deceptive data. Models trained under this method have greatly surpassed other state-of-the-art models in terms of robustness, scientists said. Machines outperform humans in many data-processing tasks, but sometimes fall victim to obvious mistakes that humans can see a mile away. Scientists at the U.S. Army Combat Capabilities Development Command's Army Research Laboratory designed a new approach that makes it harder for adversaries to trick machine learning models. "We were able to reduce model complexity by about a factor of 10 without affecting other performance metrics under benign conditions," said Army scientist Dr. Ananthram Swami.
Dr David Levy, an expert on artificial intelligence (AI), said in an interview with the Daily Star that the way technology is developing nowadays, the world might soon face a serious challenge from AI-equipped robots of all types. Levy complained that governments are acting too slowly when it comes to introducing new laws and addressing the new challenge, recalling how the Dutch Ministry of Defence had ignored his warnings regarding the potentially malign use of drones and, less than a year later, simple drones were able to paralyse the work of Gatwick Airport in the UK during Christmas season. The AI expert argues that in order to draw more attention to the problem, a "Greta Thunberg of the robot world" is needed, adding that dangerous robots are likely to harm humanity sooner than climate change. Thunberg, a famous teenage environmental activist, rose to prominence globally in recent years by rallying students around the world to take part in her "strikes for climate", which are designed to draw attention to ecological problems. She also made an appearance at a UN committee devoted to the topic, delivering a passionate speech in which she accused global leaders of doing too little to address climate change.
Advances in technology for observing the earth from space have resulted in the formation of a new company which will bring a range of innovative satellite intelligence and data services to market. With offices in Bristol, UK and in Abu Dhabi, UAE, 4 Earth Intelligence has been established to focus on new sectors and technical innovations using machine learning and Artificial Intelligence to provide smart data – in particular for global environmental applications. "Over the years our technical team has been at the forefront of the development of new techniques in machine learning and Artificial Intelligence used to process data collected by satellites which are equipped with increasingly sophisticated sensors," says David Critchley, CEO, 4 Earth Intelligence. "Having witnessed an exponential increase in the demand for new solutions to address a variety of issues affecting the planet we will lead the way in the use of space and remote sensing technologies to address challenges such as climate change, pollution and population pressure." With several flagship projects already completed around the world, the company has set a new course and 4 Earth Intelligence will capitalise on this pioneering work.
An unbiased approach to salary increases, promotions, and hiring are some of the reasons why artificial intelligence-enabled robots will make good managers in the workforce, a new study has discovered. Results from the second annual AI at Work study, conducted by Oracle and Future Workplace, revealed that the use of AI is becoming more prominent, with 50 per cent of workers currently using some form of AI at work, compared to only 32 per cent last year. At 65 per cent, the majority of workers in the study said that they are optimistic, excited and grateful about having robot co-workers, and nearly 25 per cent report having a loving and gratifying relationship with AI at work. In addition, 64 per cent of people said that they would trust a robot more than their manager, and over half said that they have turned to a robot instead of their manager for advice. Also, 82 per cent of workers think that robots can do things better than their managers.
There's been a lot of talk about the shortage of data scientists and engineers, and unfortunately, the problem is going to get worse before it gets better. When you consider the increasing demand for Artificial Intelligence (AI) expertise in all types of businesses and the role that AI is playing in making companies more competitive, there's no question that it's a serious issue. We're seeing AI applications across industries, in situations as diverse as saving the environment, predicting who will be re-admitted to hospitals or which medical device might fail, and it seems like use cases keep on coming. As Andrew Ng, a noted computer scientist, was quoted as saying, "I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years." And, industry statistics bear that out.
In the last few years, we've been experimenting with humanlike, Artificial Intelligence-based avatars. We integrated these avatars into L&D environments, workplaces, and the academic field. These avatars can act both as personal mentors (or trainers) and as clients or workers in real-world simulations. We wanted to share some of our insights. We believe AI-based trainers are going to dramatically change corporate learning, and empower both trainers and learners.