Artificial intelligence is rapidly changing many aspects of how we work and live. (How many stories did you read last week about self-driving cars and job-stealing robots? Perhaps your holiday shopping involved some AI algorithms, as well.) But despite the constant flow of news, many misconceptions about AI remain.
AI doesn’t think in our sense of the word at all, Scriffignano explains. “In many ways, it’s not really intelligence. It’s regressive.”
IT leaders should make deliberate choices about what AI can and can’t do on its own. “You have to pay attention to giving AI autonomy intentionally and not by accident,”
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Tomi Engdahl says:
How the sigmoid function is used in AI
https://www.edn.com/electronics-blogs/math-is/4461850/How-the-sigmoid-function-is-used-in-AI?utm_content=buffer427dd&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer
today’s modern world of artificial intelligence (AI), the sigmoid function is used in artificial neural networks (Reference 6) to determine the relationships between biological and artificial neural networks.
Tomi Engdahl says:
AI Vet Pushes for Neuromorphic Chips
Learning algorithm is missing link for spiking networks
https://www.eetimes.com/document.asp?doc_id=1334679
Kwabena Boahen believes a better AI is imminent.
The Stanford professor is one of dozens of researchers working on chips modeled on the human brain. They promise to handle orders of magnitude greater computations than today’s processors with a fraction of their power consumption.
Braindrop, his latest chip, beats Nvidia’s Tesla GPUs in energy efficiency and also outpaces similar processors from academics. He is already working to secure funding for a next-generation effort that could do even better, probably using ferroelectric FETs at Globalfoundries.
Tomi Engdahl says:
AI Doesn’t Know When It’s a Jerk
https://www.eetimes.com/author.asp?section_id=36&doc_id=1334685
“Fairness” strikes many in the engineering community as a nebulous, uncomfortable topic. It seems to them irrelevant to what they do in technology development and hardware/software designs. But is it?
The public perception of privacy is rapidly evolving in the United States.
American consumers, perhaps belatedly, are waking to the price to pay when they give up on their privacy. By leaving their personal data in the hands of giant tech platform companies, such as Facebook and Google, who may or may not do the ethical thing, Americans are finding out that there is little recourse when personal data gets hacked, mined, sold, or even used by suspicious groups to tip the balance of an election.
In contrast, AI’s “fairness” stands at a point where privacy discussions stood 20 years ago. It hasn’t exactly risen to the consciousness of many people. At least, not yet.
I realize that “fairness” strikes many in the engineering community as a nebulous, uncomfortable topic. It seems to them irrelevant to what they do in technology development and hardware/software designs.
But is it?
Some of our readers deemed the topic of AI fairness, discussed in an EE Times Special Project, as “social engineering.”
Many such comments imply that engineers are being asked to manipulate the technology (or game the algorithms or datasets), altering machine learning results for the sake of “politically correctness,” a term just as loaded as “fairness.”
Nothing could be further from the truth.
Fired by AI or killed by AI
The real issue is “bias,” insinuated into data sets that could skew machine learning results. An optimization strategy by training algorithms can then further amplify bias.
Fired by AI or killed by AI
The real issue is “bias,” insinuated into data sets that could skew machine learning results. An optimization strategy by training algorithms can then further amplify bias.
The market is eager for AI because every business is looking for ways to automate certain parts of their business. In pursuit of automation, we are beginning to cede to machines, wittingly or unwittingly, whole realms of decision making. These tasks include hiring, credit scoring, customer services and even driving.
Once you feel wronged by machines, you’re likely to entertain a measure of outrage, perhaps even greater than you would if you were fired by a boss whom you know is a jerk.
Can an algorithm be a jerk?
Black-box algorithms
This question poses the disconcerting reality that every AI algorithm is a black box. With no clue about what the algorithms are doing – whether they are used by a social network behemoth like Facebook or operating in a Waymo robocar – makes everything about this brave new AI era opaque and uncertain.
In recent days, people have begun openly discussing that it’s time to break up Facebook.
Tomi Engdahl says:
AI Vet Pushes for Neuromorphic Chips
Learning algorithm is missing link for spiking networks
By Rick Merritt, 05.10.19 2
https://www.eetimes.com/document.asp?doc_id=1334679
Kwabena Boahen believes a better AI is imminent.
The Stanford professor is one of dozens of researchers working on chips modeled on the human brain. They promise to handle orders of magnitude greater computations than today’s processors with a fraction of their power consumption.
Braindrop, his latest chip, beats Nvidia’s Tesla GPUs in energy efficiency and also outpaces similar processors from academics. He is already working to secure funding for a next-generation effort that could do even better, probably using ferroelectric FETs at Globalfoundries.
Tomi Engdahl says:
Why Machine Learning Is Important to Embedded
https://www.designnews.com/electronics-test/why-machine-learning-important-embedded/3392145060759?ADTRK=InformaMarkets&elq_mid=8660&elq_cid=876648
Machine learning is opening up new features and applications that will forever change how users expect their systems to behave.
Machine learning for embedded systems has been gaining a lot of momentum over the past several years. For embedded developers, machine learning was something that data scientists were concerned with and something that lived up on the cloud, far from the resource-constrained microcontrollers that embedded developers work with on a daily basis.
What seems like almost overnight, however, machine learning is suddenly finding its way to microcontroller and edge devices. To some developers, this may seem baffling or at least intriguing. But why is machine learning so important to embedded developers now? Let’s explore a few possibilities.
Tomi Engdahl says:
Most Americans Think We’re Too Reliant on Robots
https://www.designnews.com/automation-motion-control/most-americans-think-were-too-reliant-on-robots/166645757960752?ADTRK=InformaMarkets&elq_mid=8660&elq_cid=876648
A survey by YouGov finds that most Americans are not bullish about encroaching automation. They think we’re depending too much on robots.
A wide proliferation of robots and automation across industries, professions, and in homes is prompting skepticism among US consumers.
Your Income Can Affect Your View of Robots
A closer look at the data reveals that consumers earning $49,999 or less per year are slightly more likely than average to show concern: 67% say we should stop depending on robots so much. At the other end of the income spectrum, consumers earning $90,000 or more are slightly less worried about the increasing reliance on automation. Yet the majority (55%) are still concerned.
Tomi Engdahl says:
NIST Requests Information on Artificial Intelligence Technical Standards and Tools
https://www.nist.gov/news-events/news/2019/05/nist-requests-information-artificial-intelligence-technical-standards-and
The U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) is seeking information about technical standards and related tools for artificial intelligence (AI). The Request for Information (RFI), published today in the Federal Register, is in response to the Feb. 11, 2019, Executive Order on Maintaining American Leadership in Artificial Intelligence. The executive order directs NIST to create a plan for federal engagement in the development of these standards and tools in support of reliable, robust and trustworthy systems that use AI technologies.
“The inputs of the U.S. stakeholder community are essential to inform development of a plan that will support continued American leadership in AI,”
Tomi Engdahl says:
There’s a Diversity Crisis in the AI Industry
https://www.designnews.com/electronics-test/theres-diversity-crisis-ai-industry/199574341060780?ADTRK=InformaMarkets&elq_mid=8669&elq_cid=876648
A lack of racial and gender diversity at the companies creating AI ties closely with issues of bias and racial discrimination in artificial intelligence algorithms, according to a new NYU study.
Tomi Engdahl says:
Taylor Hatmaker / TechCrunch:
San Francisco passes a ban on the use of facial recognition technology by city agencies, the first ban of its kind for a major American city — On Tuesday, San Francisco’s Board of Supervisors voted to approve a ban on the use of facial recognition tech by city agencies.
San Francisco passes city government ban on facial recognition tech
https://techcrunch.com/2019/05/14/san-francisco-facial-recognition-ban/
On Tuesday, San Francisco’s Board of Supervisors voted to approve a ban on the use of facial recognition tech by city agencies, including the police department. The Stop Secret Surveillance Ordinance, introduced by San Francisco Supervisor Aaron Peskin, is the first ban of its kind for a major American city and the seventh major surveillance oversight effort for a municipality in California.
“I want to be clear — this is not an anti-technology policy,”
Importantly, the ordinance also includes a provision that would require city departments to seek specific approval before acquiring any new surveillance equipment. The ban would not impact facial recognition tech deployed by private companies, though it would affect any companies selling tech to the city government.
Tomi Engdahl says:
AI translation boosted eBay sales more than 10 percent
Research shows positive effects of AI tools on international trade
https://www.theverge.com/2019/5/15/18624459/ai-translation-boosted-ebay-sales-more-than-10-percent
We often hear that artificial intelligence is important for economic growth, and while that claim makes intuitive sense, there isn’t a lot of hard data to back it up. A recent study from economists at MIT and Washington University in St. Louis offers some proof, though, showing how AI tools boost trade by allowing sellers to cross the language barrier.
Looking at data scraped from eBay, the researchers compared sales between the US and Spanish-speaking Latin American countries before and after the shopping platform introduced AI-powered translation for product listings in 2014. (Specifically, the translation tool affected the titles of listings and search queries, but not product descriptions.)
Prior to this eBay offered automatic translation, but the use of AI significantly improved the service’s accuracy. You would expect that better translations would lead to greater sales, and that’s exactly what the researchers found. Their data showed that sales from the US to countries affected increased 10.9 percent after the launch of the tool.
Does Machine Translation Affect International Trade?
https://olinblog.wustl.edu/wp-content/uploads/2019/05/SSRN-id3210383.pdf?_ga=2.154387902.702901824.1557909289-193950373.1557909289
Tomi Engdahl says:
AI bots need a sense of hearing to navigate their computer world and the real world – eggheads
Audio perception enhances visual cues, boffins explain to El Reg
https://www.theregister.co.uk/2019/05/16/ai_bots_audio_hearing/
Tomi Engdahl says:
Kaveh Waddell / Axios:
Data from CB Insights shows Apple, Google, Microsoft, Facebook, and Amazon have collectively acquired more than 50 AI companies since 2010 — Big Tech has snapped up more than 50 AI companies since 2010, carving out another front in the nonstop war among the giants for AI talent, data and ideas.
The AI acquisitions war
https://www.axios.com/ai-acquisitions-war-79ee3c6a-dcab-40d2-b574-0d2646fe4a46.html
Big Tech has snapped up more than 50 AI companies since 2010, carving out another front in the nonstop war among the giants for AI talent, data and ideas.
The big picture: The clamor reflects a scarcity of AI expertise, as we’ve reported in the past. But it also allows Big Tech companies to reinforce their advantage over the upstarts, each time making it harder for a new entrant to strike gold.
What’s happening: Several of the top AI researchers and most lucrative products at leading tech firms came from acquisitions, according to data compiled by CB Insights.
Tomi Engdahl says:
Google AI Blog:
Google details Translatotron, its project to directly translate speech from one language into speech in another without intermediate text — Posted by Ye Jia and Ron Weiss, Software Engineers, Google AI — Speech-to-speech translation systems have been developed over the past several decades …
Introducing Translatotron: An End-to-End Speech-to-Speech Translation Model
https://ai.googleblog.com/2019/05/introducing-translatotron-end-to-end.html
In “Direct speech-to-speech translation with a sequence-to-sequence model”, we propose an experimental new system that is based on a single attentive sequence-to-sequence model for direct speech-to-speech translation without relying on intermediate text representation. Dubbed Translatotron, this system avoids dividing the task into separate stages, providing a few advantages over cascaded systems, including faster inference speed, naturally avoiding compounding errors between recognition and translation, making it straightforward to retain the voice of the original speaker after translation, and better handling of words that do not need to be translated (e.g., names and proper nouns).
https://arxiv.org/abs/1904.06037
Tomi Engdahl says:
What Deep Learning Means for CyberSecurity
https://www.securityweek.com/what-deep-learning-means-cybersecurity
Tomi Engdahl says:
. EDA-talo Cadence on nyt esitellyt uuden DSP-prosessorin, joka kasvattaa näiden laitteiden AI-suorituskyvyn kaksi kertaa aiempaa tehokkaammaksi.
Vision Q7 -prosessori on Cadencen Tensilica-ryhmän kuudennen polven signaaliprosessori kuvankäsittelyyn.
http://www.etn.fi/index.php/13-news/9478-tekoalylaskennan-teho-laitteissa-kasvoi-juuri-kaksinkertaiseksi
Tomi Engdahl says:
Bérénice Magistretti / Forbes:
Microsoft’s AI for Accessibility initiative announces seven new grantees, including a chatbot for the cognitively disabled and a nerve-sensing wearable — Today is the eighth annual Global Accessibility Awareness Day, an event that was designed to help spread awareness around …
Microsoft’s AI For Accessibility Announces New Grantees: From Chatbots To Nerve-Sensing Wearables
https://www.forbes.com/sites/berenicemagistretti/2019/05/16/microsofts-ai-for-accessibility-announces-new-grantees-chatbots-nerve-sensing-wearables/#5a6c6ea4130f
Today is the eighth annual Global Accessibility Awareness Day, an event that was designed to help spread awareness around the importance of digital accessibility and the role technology can play to empower people with disabilities. According to the World Bank, 15% of the world population lives with some form of disability, which amounts to about 1 billion individuals. Yet only 1 in 10 people have access to the assistive tools and services they need to live a more functional and independent life.
One company that is known for its inclusive initiatives is Microsoft.
Tomi Engdahl says:
Tricking Neural Networks: Create your own Adversarial Examples
https://medium.com/@ml.at.berkeley/tricking-neural-networks-create-your-own-adversarial-examples-a61eb7620fd8
neural networks could be trained to pilot drones or operate other weapons of mass destruction, but even an innocuous (and presently available) network trained to drive a car could be turned to act against its owner. This is because neural networks are extremely susceptible to something called adversarial examples.
A small amount of carefully constructed noise was added to an image that caused a neural network to misclassify the image, despite the image looking exactly the same to a human.
We will be trying to trick a vanilla feedforward neural network that was trained on the MNIST dataset. MNIST is a dataset of 28×28 pixel images of handwritten digits.
Protecting Against Adversarial Attacks
Awesome! We’ve just created images that trick neural networks. The next question we could ask is whether or not we could protect against these kinds of attacks.
Turns out binary thresholding works! But this way of protecting against adversarial attacks is not very good.
Another more general thing we could try to do is to train a new neural network on correctly labeled adversarial examples as well as the original training test set.
Tomi Engdahl says:
Uusi piiri siirtää tekoälyn pilvestä laitteeseen
http://etn.fi/index.php?option=com_content&view=article&id=9483&via=n&datum=2019-05-17_15:31:22&mottagare=31202
Tekoäly tarkoittaa tällä hetkellä pääosin sitä, että päätelaitteesta lähetetään dataa pilvipalveluun, jossa tarvittava laskenta tehdään. Israelilainen Hailo on nyt esitellyt piirin, jota se kehuu maailman tehokkaimmaksi syväoppimisprosessoriksi.
Tomi Engdahl says:
Tekoälyrekka kiertää ja jakaa tietoa
http://www.etn.fi/index.php/13-news/9486-tekoalyrekka-kiertaa-ja-jakaa-tietoa
https://tekoalyrekka.fi/
Tomi Engdahl says:
https://www.uusiteknologia.fi/2019/05/20/koneoppiminen-mahtuu-mikro-ohjaimeen/
Tomi Engdahl says:
New AI Computing in Consumer Electronics
https://www.eetimes.com/author.asp?section_id=36&doc_id=1334710
The market for AI processors is vast, but it can be organized into segments, and those segments can be targeted.
Tomi Engdahl says:
3 Ways AI Projects Get Derailed, and How to Stop Them
https://www.designnews.com/automation-motion-control/3-ways-ai-projects-get-derailed-and-how-stop-them/75927031060788?ADTRK=InformaMarkets&elq_mid=8707&elq_cid=876648
The rate of companies implementing AI is continuing to skyrocket. Don’t fall victim to wasted time and a blown budget
In the blink of an eye, AI has gone from novelty to urgency. Tech leaders are telling companies they need to adopt AI now or be left behind. And a recent Gartner survey shows just that: AI adoption has skyrocketed over the last four years, with a 270 percent increase in the percentage of enterprises implementing AI during that period.
However, the same survey shows that 63 percent of organizations still haven’t implemented AI or machine learning (ML) in some form.
Why are there so many organizations falling behind the curve?
1.) Don’t Task Your A Team with Rookie Work
2.) Get with the Times; Get Agile
3.) Don’t Underestimate the Challenge of Training Data
Companies can get their AI and machine learning models to production faster and on-budget by setting clear expectations and responsibilities for its data science team, using the agile approach, and preparing for the challenge of training datasets. If the rate of companies implementing AI continues to skyrocket, a realistic view of what it takes to get a model to an appropriate confidence level will be vital. Don’t fall victim to wasted time and a blown budget.
Tomi Engdahl says:
Face It, You’re Being Watched
https://www.youtube.com/watch?v=IbTBh7Uc450
San Francisco is the first American city to ban facial recognition software used by police and other agencies. Bloomberg QuickTake explains why the technology’s advance is so alarming to regulators, the public, and even the people developing it.
Tomi Engdahl says:
DARPA Spent $1 Billion Trying to Build a Real-Life Skynet in the 1980s
https://paleofuture.com/blog/2019/4/30/darpa-spent-1-billion-trying-to-build-a-real-life-skynet-in-the-1980s
Tomi Engdahl says:
Artificial Intelligence and Cybersecurity
https://pentestmag.com/artificial-intelligence-and-cybersecurity/
The Crossroads of Artificial Intelligence, Machine Learning, and Deep Learning
What is Artificial Intelligence, Deep Learning, and Machine Learning?
Think of artificial intelligence (AI), deep learning (DL) and machine learning (ML) as the layers of an onion. Starting with the outer layer of the onion (Figure 1) as AI, as you move through the layers, you encounter ML, and then DL, which is a subset of machine learning.
The term artificial intelligence is frequently used as a marketing product term by many cybersecurity companies without consensus about what it means.
Currently, AI can only do the task it was designed to perform, and specific algorithms are developed to solve problems. At this point, AI does not understand what it was trained to do, but the future of AI is to design systems that can learn and then solve any problem.
AI is a collection of technology that a spectrum of industries utilizes
A powerful technique used for cybersecurity technology is machine learning.
Machine Learning (ML) are mathematical techniques that enable information mining, pattern discovery and drawing inferences from data (Chio,2018). Think about machine learning as a part of AI, but AI does not always utilize machine learning methods. ML can discern patterns from examining raw data and can then use models to make predictions.
Machine learning refers to an algorithm that can create abstractions (models) by training on a dataset and is a method of training an algorithm to accomplish a task. Training involves providing large data sets to the algorithm so the algorithm can adjust and improve. Machine learning modifies itself when exposed to more data. The learning part of machine learning refers to ML algorithms optimizing along a dimension, such as trying to minimize error or enhance the likelihood of predictions becoming true
Tomi Engdahl says:
http://www.etn.fi/index.php/72-ecf/9495-koneoppiminen-mullistaa-sulautetun-tekniikan
Tomi Engdahl says:
Opaskirjanen suomenkieliseen puheentunnistukseen
https://www.uusiteknologia.fi/2019/05/21/opaskirjanen-suomenkieliseen-puheentunnistukseen/
Suomen Tekoälykiihdyttämö on julkaissut puheentunnistuksen hyödyntämiseen ohjekirjan, jonka avulla avulla organisaatiot voivat ottaa ensimmäiset askeleet luonnollisen kielen prosessointiin. Oppaan voi ladata veloituksetta julkaisijan nettisivulta.
Your guide to speech processing
https://faia.fi/playbook/
Tomi Engdahl says:
Is AI Safety a Pascal’s Mugging?
https://www.youtube.com/watch?v=JRuNA2eK7w0
An event that’s very unlikely is still worth thinking about, if the consequences are big enough. What’s the limit though?
Do we have to devote all of our resources to any outcome that might give infinite payoffs, even if it seems basically impossible? Does the case for AI Safety rely on this kind of Pascal’s Wager argument? Watch this video to find out that the answer to these questions is ‘No’.
Tomi Engdahl says:
http://www.etn.fi/index.php/13-news/9510-binaarivektoreista-muisti-robotille
Tomi Engdahl says:
From https://semiengineering.com/week-in-review-iot-security-auto-46/
The Artificial Intelligence Initiative Act, bipartisan legislation introduced in the U.S. Senate, would provide $2.2 billion in AI research and development funding for the next five years. The National Science Foundation would receive $500 million for research and new educational standards and institutions. The National Institute of Standards and Technology would get $40 million to establish AI algorithm benchmarks. The Department of Energy would be given $1.5 billion to set up five AI R&D centers. The White House has requested about $850 million for AI R&D.
Tomi Engdahl says:
Deadly Truth of General AI? – Computerphile
https://www.youtube.com/watch?v=tcdVC4e6EV4
The danger of assuming general artificial intelligence will be the same as human intelligence. Rob Miles explains with a simple example: The deadly stamp collector.
Tomi Engdahl says:
What can AGI do? I/O and Speed
https://www.youtube.com/watch?v=gP4ZNUHdwp8
Suppose we make an algorithm that implements general intelligence as well as the brain. What could that system do?
It might have better input and output than a human, and probably could be run faster…
Tomi Engdahl says:
Are AI Risks like Nuclear Risks?
https://www.youtube.com/watch?v=1wAgBaJgEsg
Concerns about AI cover a really wide range of possible problems. Can we make progress on several of these problems at once?
Tomi Engdahl says:
Predicting AI: RIP Prof. Hubert Dreyfus
https://www.youtube.com/watch?v=B6Oigy1i3W4
It’s hard to predict what AI will be like in the future. Many tried in the past, and all failed to some extent. In this video we look at Professor Hubert Dreyfus, and one of his reasons for thinking AI couldn’t be done.
Tomi Engdahl says:
AI Safety Gridworlds
https://www.youtube.com/watch?v=CGTkoUidQ8I
Got an AI safety idea? Now you can test it out! A recent paper from DeepMind sets out some environments for evaluating the safety of AI systems, and the code is on GitHub.
https://github.com/deepmind/ai-safety-gridworlds
Tomi Engdahl says:
AI learns to Create Cat Pictures: Papers in Two Minutes #1
https://www.youtube.com/watch?v=MUVbqQ3STFA
Some beautiful new GAN results have been published, so let’s have a quick look at the pretty pictures.
Generative Adversarial Networks (GANs) – Computerphile
https://www.youtube.com/watch?v=Sw9r8CL98N0
Artificial Intelligence where neural nets play against each other and improve enough to generate something new. Rob Miles explains GANs
Tomi Engdahl says:
AI “Stop Button” Problem – Computerphile
https://www.youtube.com/watch?v=3TYT1QfdfsM
How do you implement an on/off switch on a General Artificial Intelligence? Rob Miles explains the perils.
Tomi Engdahl says:
What’s the Use of Utility Functions?
https://www.youtube.com/watch?v=8AvIErXFoH8
A lot of our problems with AI seem to relate to its utility function. Why do we need one of those, anyway?
Comments:
5:47 “Human intelligence is kind of badly implemented”. Damm god, you had one job!
“If your preferences aren’t transitive, you can get stuck in a loop”
You’re assuming a utility function has to be a total ordering of world states: partial orderings are also consistent but are not amenable to being numbered.
Tomi Engdahl says:
NVIDIA’s AI Creates Beautiful Images From Your Sketches
https://www.youtube.com/watch?v=hW1_Sidq3m8
The paper “Semantic Image Synthesis with Spatially-Adaptive Normalization” and its source code is available here:
https://nvlabs.github.io/SPADE/
https://github.com/NVlabs/SPADE
Tomi Engdahl says:
Home> Test-and-measurement Design Center > How To Article
Will AI come to the test industry?
https://www.edn.com/design/test-and-measurement/4461906/Will-AI-come-to-the-test-industry-
Tomi Engdahl says:
How AI and Blockchain Will Transform the Job Market
https://www.eeweb.com/profile/ethan-bennett/articles/how-ai-and-blockchain-will-transform-the-job-market
While blockchain and AI are here to stay, concerns have been raised about the changes that they bring to the U.S. job market and whether the country is ready to handle such change.
Tomi Engdahl says:
AI Lights Up Hot Chips
Cerebras discloses its anticipated wafer-scale integration work
https://www.eetimes.com/document.asp?doc_id=1334742
In a sign of the times, half of the talks at this year’s Hot Chips are focused on AI acceleration. The annual gathering for microprocessor designers once focused most of its talks on CPUs for PCs and servers.
Startups Cerebras, Habana, and UpMem will unveil new deep-learning processors. Cerebras will describe a much-anticipated device using wafer-scale integration. Habana, already shipping an inference chip, will show its follow-on for training.
Grenoble-based UpMem will disclose a new processor-in-memory, believed to be using DRAM, aiming at multiple uses. Graphcore was invited but was not ready to share more details of its chips.
The startups will compete with giants such as Intel, which is describing Spring Hill and Spring Crest, its inference and training chips based on its Nervana architecture. In a rare appearance, Alibaba will disclose an inference processor for embedded systems.
Tomi Engdahl says:
Mentor Juices AI Chips with AI
https://www.eetimes.com/document.asp?doc_id=1334736
Tomi Engdahl says:
AI Enables Early Predictions of Li-Ion Battery Life
https://www.electronicdesign.com/power/ai-enables-early-predictions-li-ion-battery-life?sfvc4enews=42&cl=article_2_b&utm_rid=CPG05000002750211&utm_campaign=25744&utm_medium=email&elq2=9e811b115770496ab9d7dd977b899c5c&oly_enc_id=0452E0081834E9U
Using artificial intelligence on voluminous data from controlled lithium-ion battery tests, researchers devised algorithms that assess battery-life expectancy based on early-cycle performance.
Tomi Engdahl says:
3 Ways AI Projects Get Derailed, and How to Stop Them
https://www.designnews.com/automation-motion-control/3-ways-ai-projects-get-derailed-and-how-stop-them/75927031060788?ADTRK=InformaMarkets&elq_mid=8794&elq_cid=876648
The rate of companies implementing AI is continuing to skyrocket. Don’t fall victim to wasted time and a blown budget.
Tomi Engdahl says:
Frederic Lardinois / TechCrunch:
ARM announces the Cortex-A77 CPU and Mali-G77 GPU touting performance improvements, and a more energy efficient and powerful machine learning processor
Arm announces its new premium CPU and GPU designs
https://techcrunch.com/2019/05/26/arm-announces-its-new-premium-cpu-and-gpu-designs/
Tomi Engdahl says:
IDC:
Asia Pacific spending on AI will reach $5.5B in 2019, up 80% YoY, and will reach $15B in 2022, led by the retail industry
IDC Expects Asia/Pacific* Artificial Intelligence Systems Spending to Reach Nearly USD 5.5 Billion in 2019
https://www.idc.com/getdoc.jsp?containerId=prAP45089819
According to the latest Worldwide Semiannual Artificial Intelligence Systems Spending Guide, Asia/Pacific* spending on artificial intelligence (AI) systems is forecast to reach nearly USD 5.5 billion in 2019, an increase of almost 80% in spent versus 2018. As industries invest aggressively in projects that utilize AI software capabilities, IDC expects spending on AI systems will increase to USD 15.06 billion in 2022 with a compound annual growth rate (CAGR) of 50% over the 2018-2022 forecast period.
Tomi Engdahl says:
How to prevent an AI-induced mid-career crisis
https://thenextweb.com/podium/2019/05/26/how-to-ride-the-ai-wave-when-youre-mid-career/
At this point, it feels like I’ve heard countless stories about workers who are restless about the effects that AI will have on their jobs and their careers in the relatively near future.
While we must certainly take into account that the nature of certain workplaces and the human roles within them may change, I believe a proactive approach in adapting can make all the difference in withstanding AI’s advent in the workforce.
There are multiple AI waves happening simultaneously, though the one that directly affects the workforce is known as “business AI.” This form of artificial intelligence is trained to sort through data sets and use deep-learning algorithms to help make better business decisions and maximize efficiency.
Business AI encompasses many of the systems that are threatening jobs — they are single domain, meaning they can only perform a limited amount of tasks, but the tasks they can do are completed with speed and accuracy.
Improve soft skills
AI is already capable and will continue to grow even more complex. But until the “general AI” wave comes, AI will likely remain incapable of replicating the “human factor” of working a job. So if you’re an ambitious problem solver or an excellent team player, those are strengths that you explore and develop.
Many employers offer upskilling in topics relevant to the increasingly technological workplace. For example, AT&T has dedicated $1 billion to retraining employees in data science, cybersecurity, and other tech-related topics.
Leverage AI to improve work quality
In many cases, AI will serve to improve a person’s job instead of replace it. Financial analysts, for instance, can spend nearly half of their workdays taking on lesser tasks such as processing financial data or creating and updating charts for potential investments, which would otherwise leave little room for meaningful productivity among workers.
AI can perform these duties in a fraction of the time it takes humans; but instead of making us obsolete, I believe it will enable employees to take on more important responsibilities
Approximately one in five workers are expected to have an AI co-worker by 2022, meaning employees should be attentive of the ways automation can free up their workload and how they can effectively spend their newly gained time. With AI machines doing the heavy lifting, mid-career positions will feel much more meaningful and fulfilling.
Despite negative perceptions of AI, automation will lead to overall growth. New technologies have historically increased productivity, employment growth, and wage growth: PwC predicts that AI and automation will contribute approximately $16 trillion to the global GDP, a 14 percent increase, by 2030.
Tomi Engdahl says:
The Future of Military (Artificial) Intelligence
https://www.designnews.com/electronics-test/future-military-artificial-intelligence/165978317459765?ADTRK=InformaMarkets&elq_mid=8839&elq_cid=876648
In a free webinar, AI, military, and law enforcement experts spoke with Design News about how the defense industry is using artificial intelligence in warfare and beyond and what the future may hold.
Tomi Engdahl says:
If DARPA Has Its Way, AI Will Rule the Wireless Spectrum
https://spectrum.ieee.org/telecom/wireless/if-darpa-has-its-way-ai-will-rule-the-wireless-spectrum