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) …
Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders, such as Alzheimer's disease (AD). It measures minute changes in blood oxygen levels within the brain over time, giving insight into the local activity of neurons; however, fMRI has not been widely used in clinical diagnosis. Their limited use is due to the fact fMRI data are highly susceptible to noise, and the fMRI data structure is very complicated compared to a traditional x-ray or MRI scan. Scientists from Texas Tech University now report they developed a type of deep-learning algorithm known as a convolutional neural network (CNN) that can differentiate among the fMRI signals of healthy people, people with mild cognitive impairment, and people with AD. Their findings, "Spatiotemporal feature extraction and classification of Alzheimer's disease using deep learning 3D-CNN for fMRI data," is published in the Journal of Medical Imaging and led by Harshit Parmar, doctoral student at Texas Tech University.
The goal of this blog is to cover the key topics to consider in operationalizing machine learning and to provide a practical guide for navigating the modern tools available along the way. To that end, the subsequent blogs will include further detailed architecture concepts and help you apply them to your own model pipelines. This blog series will not explain machine learning concepts but rather to tackle the auxiliary challenges like dealing with large data sets, computational requirements and optimizations, and the deployment of models and data to large software systems. Most classical software applications are deterministic where the developer writes explicit lines of code that encapsulate the logic for the desired behavior. Whereas, the ML software applications are probabilistic where the developer writes a more abstract code and lets the computer write the code in a human unfriendly language i.e. the weights or parameters required for the ML model.
What's New: Today, Intel announced it will acquire SigOpt, a San Francisco-based provider of a leading platform for the optimization of artificial intelligence (AI) software models at scale. SigOpt's AI software technologies deliver productivity and performance gains across hardware and software parameters, use cases and workloads in deep learning, machine learning and data analytics. Intel plans to use SigOpt's software technologies across Intel's AI hardware products to help accelerate, amplify and scale Intel's AI software solution offerings to developers. "In the new intelligence era, AI is driving the compute needs of the future. It is even more important for software to automatically extract the best compute performance while scaling AI models. SigOpt's AI software platform and data science talent will augment Intel software, architecture, product offerings and teams, and provide us with valuable customer insights. We welcome the SigOpt team and its customers to the Intel family."
The job market is governed by one constant--change. Sometimes that change can be anticipated, like seasonal recruitment cycles. In many countries across the world, lockdown restrictions are slowly being relaxed and economies are beginning to reawaken. Consequently, companies can expect to receive a high volume of job applications from candidates eager to return to work. To effectively handle this surge in hiring, talent teams should incorporate AI recruitment tools into their workflows as much as possible.
The 2020 presidential election presents two stark paths for the direction of future-focused scientific research, I write with my Axios colleague Alison Snyder. Why it matters: Science is a long game, with today's breakthroughs often stemming from research carried out decades ago, often with government help. That means the person who occupies the White House over the next four years will help shape the state of technology for decades into the future. Where it stands: The Trump administration's record on science is criticized by experts in nearly every field, from climate change to biotechnology to health, who sense that science as a practice has been deprioritized and politicized. Yes, but: Two research areas prioritized under the Trump administration -- AI and quantum information sciences (QIS) -- are at the heart of technonationalism and the global science race, particularly between the U.S. and China.
AI and Machine learning concepts are gaining a lot of traction, and these hot trends in technology can transform the way businesses work, mainly in the B2C landscape. It's no surprise that AI is getting the entire spotlight regarding B2C companies than B2B business, mostly because of the significantly larger consumer base. Through AI's profound impact on enhancing customer experience, these technologies transform the digital era's shopping landscape. However, B2B business isn't exempt from the AI revolution; many B2B business operations have their set of challenges which can be nullified or optimized with this latest technology. One of the many ways AI transforms business is by working to establish higher merchandise.
Unconscious biases are pervasive in text and media. For example, female characters in stories are often portrayed as passive and powerless while men are portrayed as more proactive and powerful. According to a McKinsey study of 120 movies across ten markets, the ratio of male to female characters was 3:1 in 2016, the same it's been since 1946. Motivated by this, researchers at the Allen Institute for Artificial Intelligence and the University of Washington created PowerTransformer, a tool that aims to rewrite text to correct implicit and potentially undesirable bias in character portrayals. They claim that PowerTransformer is a major a step toward mitigating well-documented gender bias in movie scripts, as well as other scripts in other forms of media.
In the end most teams used smaller models that produced specific parts of a song, like the chords or melodies, and then stitched these together by hand. Uncanny Valley used an algorithm to match up lyrics and melodies that had been produced by different AIs, for example. Another team, Dadabots x Portrait XO, did not want to repeat their chorus twice but couldn't find a way to direct the AI to change the second version. In the end the team used seven models and cobbled together different results to get the variation they wanted. It was like assembling a jigsaw puzzle, says Huang: "Some teams felt like the puzzle was unreasonably hard, but some found it exhilarating, because they had so many raw materials and colorful puzzle pieces to put together."
It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.
If artificial intelligence is going to spread to trillions of devices, those devices will have to operate in a way that doesn't need a human to run them, a Google executive who leads a key part of the search giant's machine learning software told a conference of chip designers this week. "The only way to scale up to the kinds of hundreds of billions or trillions of devices we are expecting to emerge into the world in the next few years is if we take people out of the care and maintenance loop," said Pete Warden, who runs Google's effort to bring deep learning to even the simplest embedded devices. "You need to have peel-and-stick sensors," said Warden, ultra-simple, dirt-cheap devices that require only tiny amounts of power and cost pennies. "And the only way to do that is to make sure that you don't need to have people going around and doing maintenance." Warden was the keynote speaker Tuesday at a microprocessor conference held virtually, The Linley Fall Processor Conference, hosted by chip analysts The Linley Group.