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:
Top 8 open source AI technologies in machine learning
https://opensource.com/article/18/5/top-8-open-source-ai-technologies-machine-learning?sc_cid=7016000000127ECAAY
Take your machine learning to the next level with these artificial intelligence technologies.
Here is a list of 8 best open source AI technologies you can use to take your machine learning projects to the next level.
Tomi Engdahl says:
Society needs the Artificial Intelligence Data Protection Act now
https://techcrunch.com/2018/05/15/society-needs-the-artificial-intelligence-data-protection-act-now/?utm_source=tcfbpage&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&sr_share=facebook
Since 2015, we have witnessed AI’s rapidly evolving national and international growth and adoption that will soon impact every phase of mankind’s life, from birth to death, sex to religion, politics to war, education to emotion, jobs to unemployment.
Three of many recent developments confirm why now is the time for the AIDPA: (1) a McKinsey study from late 2017 determined that up to 800 million workers worldwide may lose their jobs to AI by 2030, half of contemporary work functions could be automated by 2055 and other recent studies suggest as many as 47 percent of U.S. jobs could be threatened by automation or AI over the next few decades; (2) AI has now created IP with little or no human involvement and continues to be programmed, tested and used to do so; see my Twitter for a library of media reports on AI-created IP; (3) tech giants and regulators are starting to acknowledge that industries that create and use AI should be at least partially responsible for minimizing the impact of AI-displaced workers.
Now – and not later — society must address AI’s legal, economic and social implications with regard to IP and employment. Current legislation does not adequately account for the new challenges, threats and needs presented by the impact of AI.
Tomi Engdahl says:
Crossbar Pushes Resistive RAM into Embedded AI
https://spectrum.ieee.org/nanoclast/semiconductors/memory/crossbar-pushes-reram-into-embedded-ai
Resistive RAM technology developer Crossbar says it has inked a deal with aerospace chip maker Microsemi allowing the latter to embed Crossbar’s nonvolatile memory on future chips. The move follows selection of Crossbar’s technology by a leading foundry for advanced manufacturing nodes. Crossbar is counting on resistive RAM (ReRAM) to enable artificial intelligence systems whose neural networks are housed within the device rather than in the cloud.
ReRAM is a variant of the memristor, a nonvolatile memory device whose resistance can be set or reset by a pulse of voltage.
Tomi Engdahl says:
AI vs Doctors
https://spectrum.ieee.org/static/ai-vs-doctors
Artificial intelligence is challenging doctors on their home turf. We’re keeping score
Tomi Engdahl says:
Stephanie Condon / ZDNet:
Intel launches OpenVINO toolkit to help developers deploy AI models across a broad range of IoT devices — The OpenVINO toolkit should make it easy for developers to deploy AI models across a broad range of IoT devices. — Intel on Wednesday is unveiling OpenVINO, a toolkit designed …
Intel launches toolkit to bring computer vision to the edge
https://www.zdnet.com/article/intel-launches-toolkit-to-bring-computer-vision-to-the-edge/
The OpenVINO toolkit should make it easy for developers to deploy AI models across a broad range of IoT devices.
Tomi Engdahl says:
Neural Networks Using Doom Level Creator Like It’s 1993
https://hackaday.com/2018/05/16/neural-networks-using-doom-level-creator-like-its-1993/
Readers of a certain vintage will remember the glee of building your own levels for DOOM. There was something magical about carefully crafting a level and then dialing up your friends for a death match session on the new map. Now computers scientists are getting in on that fun in a new way. Researchers from Politecnico di Milano are using artificial intelligence to create new levels for the classic DOOM shooter (PDF whitepaper).
generate new content by using existing, human-generated levels as a model.
https://arxiv.org/pdf/1804.09154.pdf
Tomi Engdahl says:
Startup Maps AI into Flash Array
Mythic preps low-power inference processor
https://www.eetimes.com/document.asp?doc_id=1333295
Tomi Engdahl says:
Integrating Memristors For Neuromorphic Computing
https://semiengineering.com/integrating-memristors-for-neuromorphic-computing/
The latest research on memory, variability, and compute architectures—and what comes next.
Tomi Engdahl says:
AI Benchmark Targets Inference
EEMBC to measure power-constrained chips
https://www.eetimes.com/document.asp?doc_id=1333297
The EEMBC trade group has started an effort to define a machine-learning benchmark for running inference jobs on devices at the edge of the network. The effort spun out of a separate benchmark that the group plans to release in June for chips used in advanced driver assistance systems (ADAS).
The work marks at least the third major initiative in six months to measure performance of neural-network jobs. It may be the first to focus on chips for power-constrained embedded systems.
Last month, Baidu and Facebook announced work with a handful of chipmakers on ML Perf, initially focused on training jobs in data centers. The Transaction Processing Council formed an effort in December that also likely will focus on training.
Tomi Engdahl says:
Reworking the gender balance in the AI, IoT industries
https://www.controleng.com/single-article/reworking-the-gender-balance-in-the-ai-iot-industries/9863400741d54b888b09437811be69ea.html
Women in STEM: Bringing more women into the artificial intelligence (AI) and Internet of Things (IoT) industries can help reduce some of the ingrained bias in developing these technologies and concepts.
Re•Work’s third Women in AI dinner was held in London in February 2018. This regular networking event celebrates women in artificial intelligence (AI) and showcases their achievements. Although the speakers are women, these are not women-only events. This is important, because diversity is about inclusivity, not segregation.
There are not enough women working in tech, let alone in AI. In the UK, for example, 83% of people working in science, technology, engineering and math (STEM) careers are men, according to figures presented at UK Robotics Week 2017. It has been reported that less than 10% of coders are women, despite Ada Lovelace being widely considered to be the first computer programmer.
Tomi Engdahl says:
Ashlee Vance / Bloomberg:
Interviews with pioneers of AI and PM Justin Trudeau about the history of neural networks and how the Canadian government brought the AI researchers together
Apple and Its Rivals Bet Their Futures on These Men’s Dreams
https://www.bloomberg.com/news/features/2018-05-17/apple-and-its-rivals-bet-their-futures-on-these-men-s-dreams
An oral history of artificial intelligence, as told by its godfathers, gadflies, and Justin Trudeau.
Tomi Engdahl says:
Stephanie Condon / ZDNet:
Bank of America begins rolling out its AI-powered assistant, Erica, which will help its 25M mobile clients do banking tasks by voice, text, and gestures — The virtual assistant will be available to Bank of America’s 25 million mobile clients — Bank of America on Friday officially introduced Erica …
Bank of America debuts its AI-powered assistant, Erica
https://www.zdnet.com/article/bank-of-america-debuts-its-ai-powered-assistant-erica/
The virtual assistant will be available to Bank of America’s 25 million mobile clients
Tomi Engdahl says:
Startup Raises $12 Million to Make Most of Embedded Hardware
http://www.electronicdesign.com/embedded-revolution/startup-raises-12-million-make-most-embedded-hardware
As the pace of processor development slows, many companies are betting that custom silicon can cut the cost of machine learning in embedded devices and give them independence from the internet. But even though millions of dollars are pouring into new chips, some argue there is nothing wrong with existing hardware.
The problem is that software is too rough around the edges, and increasingly investors are onboard with startups trying to change that.
“Our ‘A.I. everywhere for everyone’ technology eliminates the need for internet connectivity, runs on inexpensive hardware platforms and eliminates latency inherent in traditional cloud based A.I. systems,” said Ali Farhadi, founder and chief executive of XNOR, which previously raised $2.6 million in seed funding.
Taking machine learning out of the cloud would allow drones to scan farmland to pinpoint failing crops and recommend optimum harvest time without being connected to the internet, said XNOR. Smartwatches could measure vital signs without wasting energy to send raw data to the cloud, and smart speakers could perform simple voice recognition and control functions.
The transportation industry could also enlist XNOR’s technology.
Tomi Engdahl says:
Does my algorithm have a mental-health problem?
http://bigthink.com/aeon-ideas/does-my-algorithm-have-a-mental-health-problem?utm_campaign=Echobox&utm_medium=Social&utm_source=Facebook#link_time=1526836448
Is my car hallucinating? Is the algorithm that runs the police surveillance system in my city paranoid? Marvin the android in Douglas Adams’s Hitchhikers Guide to the Galaxy had a pain in all the diodes down his left-hand side. Is that how my toaster feels?
This all sounds ludicrous until we realize that our algorithms are increasingly being made in our own image. As we’ve learned more about our own brains, we’ve enlisted that knowledge to create algorithmic versions of ourselves.
Tomi Engdahl says:
Startup Goes Wide, Lean on AI
Revolution takes broad view of machine learning
https://www.eetimes.com/document.asp?doc_id=1333302
Startup Revolution Computing here has novel ideas both for a machine-learning accelerator and for holding down the costs of designing it. The company aims to produce for as little as $12 million its first chip based on a new architecture tuned for a broad range of data analytics algorithms.
The Revolver chip will speed up a basket of algorithms that analyze the structured databases that most businesses maintain. For example, it believes that it will boost x86 server performance more than tenfold on decision trees, random forests, ensemble methods, supportive vectors, and gradient boosting.
The algorithms serve a wide variety of use cases including fraud detection for banks, ad matching and recommendation engines for retailers, and predictive maintenance for industrial users.
The chip also will run deep-learning jobs. However, Revolution co-founder Rodney Hooker notes that many businesses don’t maintain the large unstructured image and video datasets suited for deep learning.
Tomi Engdahl says:
NY Tech Summit to address AI, IoT, cybersecurity, cloud/edge computing
https://www.cablinginstall.com/articles/2018/05/ny-tech-teracai.html?cmpid=enl_cim_cim_data_center_newsletter_2018-05-21&pwhid=6b9badc08db25d04d04ee00b499089ffc280910702f8ef99951bdbdad3175f54dcae8b7ad9fa2c1f5697ffa19d05535df56b8dc1e6f75b7b6f6f8c7461ce0b24&eid=289644432&bid=2110924
1. How AI can create real-time value for you
Presented by Michael Larche, St. Ann’s Community
Hey Google, what is artificial intelligence (AI)? AI is the machine intelligence that brought devices like Alexa, Google Assistant and smart phones into your homes (not to mention a confusing movie from Steven Spielberg). But that’s not all. AI is changing business models and modes of thinking all over the world.
Not surprisingly, it’s Gartner’s first trend on their list of Top 10 Strategic Technology Trends for 2018.
According to the Gartner report: “A 2017 Gartner survey found that 59% of organizations are still gathering information to build their AI strategies, while the rest have already made progress in piloting or adopting AI solutions.”
Tomi Engdahl says:
The new processor challenges Intel, Nvidia and everyone else
New processor architectures are not published every day, and at least those that promise to revolutionize everything from computers to graphics cards and artificial intelligence to servers and datacenters. The architecture of Silicon Valley Tachyum promises to do so.
Tachyum has released its first processor family. Prodigy promises 10 times more performance per watt than universal processors. According to the company, the server machines are so efficient that the same computing power equals one percent space and consumes ten less power than the current solutions.
Tachyum says that in 2020, the Prodigy processors are to build a supercomputer capable of simulating human brain activity in real time.
Source: http://www.etn.fi/index.php/13-news/8025-uusi-prosessori-haastaa-intelit-nvidiat-ja-kaikki-muutkin
Tomi Engdahl says:
Kenneth Falck
AI & ML in the Cloud: Managed Services 2018
https://www.amazon.com/AI-ML-Cloud-Managed-Services-ebook/dp/B07D6RN7F4/
This book offers an overview of the currently available Artificial Intelligence and Machine Learning services in the cloud. It covers the four large cloud platforms – Amazon AWS, Google Cloud, IBM Cloud and Microsoft Azure.
Tomi Engdahl says:
Arm Gives Glimpse of AI Core
One smartphone maker beta testing RTL
https://www.eetimes.com/document.asp?doc_id=1333307
Arm sketched the inner workings of its machine-learning core at a press and analyst event here. Engineers are nearly finished with RTL for the design with hopes of snagging a commitment within weeks for use in a premium smartphone in 2019 or later.
Analysts generally praised the architecture as a flexible but late response to a market that is already crowded with dozens of rivals. However, Arm still needs to show detailed performance and area numbers for the core, which may not see first silicon until next year.
The first core is aimed at premium smartphones that are already using AI accelerator blocks from startup DeePhi (Samsung Galaxy), Cambricon (Huawei Kirin), and in-house designs (iPhone). The good news for Arm is that it’s already getting some commercial traction for the neural-networking software for its cores, released as open source, that sits under frameworks such as TensorFlow.
Tomi Engdahl says:
IBM plans to hire 1,800 people in France for blockchain and AI
https://techcrunch.com/2018/05/23/ibm-plans-to-hire-1800-people-in-france-for-blockchain-and-ai/?sr_share=facebook&utm_source=tcfbpage
Tomi Engdahl says:
‘Dehumanising, impenetrable, frustrating’: the grim reality of job hunting in the age of AI
https://www.theguardian.com/inequality/2018/mar/04/dehumanising-impenetrable-frustrating-the-grim-reality-of-job-hunting-in-the-age-of-ai
The automation revolution has hit recruitment, with everything from facial expressions to vocal tone now analysed by algorithms and artificial intelligence. But what’s the cost to workforce diversity – and workers themselves?
Tomi Engdahl says:
Alibaba’s newest initiative aims to make Hong Kong a global AI hub
https://techcrunch.com/2018/05/21/alibaba-senstime-hong-kong-ai-lab/?utm_source=tcfbpage&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&sr_share=facebook
Alibaba is teaming up with SenseTime, the world’s highest-valued AI startup, to launch a not-for-profit artificial intelligence lab in Hong Kong in a bid to make the city a global hub for artificial intelligence.
Tomi Engdahl says:
Top 8 open source AI technologies in machine learning
https://opensource.com/article/18/5/top-8-open-source-ai-technologies-machine-learning
Take your machine learning to the next level with these artificial intelligence technologies.
Tomi Engdahl says:
This device lets you talk to your computer – without saying a word
https://www.weforum.org/agenda/2018/04/computer-system-transcribes-words-users-speak-silently?utm_source=Facebook%20Videos&utm_medium=Facebook%20Videos&utm_campaign=Facebook%20Video%20Blogs
MIT researchers have developed a computer interface that can transcribe words that the user concentrates on verbalizing but does not actually speak aloud.
The system consists of a wearable device and an associated computing system. Electrodes in the device pick up neuromuscular signals in the jaw and face that are triggered by internal verbalizations — saying words “in your head” — but are undetectable to the human eye. The signals are fed to a machine-learning system that has been trained to correlate particular signals with particular words.
Tomi Engdahl says:
https://techcrunch.com/2018/05/20/nvidias-researchers-teach-a-robot-to-learn-simple-tasks-by-observing-a-human/?utm_source=tcfbpage&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&sr_share=facebook
Tomi Engdahl says:
https://www.hackster.io/glowascii/archimedes-the-ai-robot-owl-325ff5
Tomi Engdahl says:
https://www.hackster.io/Nyceane/ai-facial-recognition-lock-7a3e43
Tomi Engdahl says:
AI Chip Tests Binary Approach
Imec’s Lena explores in-memory compute
https://www.eetimes.com/document.asp?doc_id=1333321
Imec said at its annual event here that it is prototyping a processor-in-memory (PIM) design for deep-learning inference using single-bit precision. The research institute hopes to gather data over the next year on the effectiveness for client devices of the novel architecture and data type.
The PIM architecture, explored by academics for decades, is gaining popularity for data-intensive machine-learning algorithms. Startup Mythic and IBM Research are designing two of the most prominent efforts in the field.
Many academics are experimenting with 1- to 4-bit data types to trim the heavy memory requirements for deep learning. So far, commercial designs for AI accelerators from Arm and others are focusing on 8-bit and larger data types, in part because programming tools such as Google’s TensorFlow lack support for the smaller data types.
Imec had the logic portion of a 40-nm accelerator built in a foundry and is now adding an MRAM layer in its own fab. It also will simulate the performance of the design using SRAM and estimated design rules for a 5-nm node.
“Our mission is to define what semiconductor technologies we should develop for machine learning using emerging memories — we may need process tweaks” to get optimal results, said Diederik Verkest, a distinguished member of technical staff in an interview.
Tomi Engdahl says:
Will Intel’s Deep Neural Processor Be Too Late?
https://www.eetimes.com/document.asp?doc_id=1333323
Intel disclosed plans to release late next year its first neural network processor intended for commercial use, but some analysts wonder if it will be too late for Intel to make up ground lost in AI to Nvidia’s Volta GPU architecture.
The Intel chip, codenamed Spring Crest, will offer three to four times the training performance of Intel’s first-generation neural network processor, Lake Crest.
Spring Crest — or the Intel Nervana NNP-L1000 as it is formally known — was the most significant of a number of innovations described by Naveen Rao, vice president and general manager of Intel’s AI Products Group, Tuesday (May 23) at Intel’s first-ever AI developer event here.
Kevin Krewell, a principal analyst at Tirias Research, said that even with three to four times the performance of Lake Crest, Spring Crest would still have only a slight performance advantage over Nvidia’s V100 data center GPUs, which are already available.
“It may take until 2020 or 2021 for Intel to potentially deliver a superior neural net processor,” Krewell said.
“Intel is late to the party and shooting at a moving target,” said Jim McGregor, founder and principal analyst at Tirias. “You can bet that the Nvidia solution will be well beyond that point by that time. It kinda sounds like Intel and GPUs all over again.”
Tomi Engdahl says:
If you do not take advantage of data, you should turn it off immediately
The bookwriter, Antti Merilehto, called on FiCom Forum today to launch all attempts to make artificial intelligence immediately. – If you do not use data in business, it’s the most energy-efficient way to shut down the lights already, Merilehto rolled.
The Merilehto has written an artificial intelligence for guide leaders ( Artificial Intelligence – Travel Guide Director ). The book contains many examples of the use of artificial intelligence. Like Google’s data center, whose energy consumption by the best professors tried to shrink. – Hey, they got cut by less than one percent. With the aid of artificial intelligence, it was cut by 15%.
Artificial intelligence helps with Merilehto to do things better than people who are good. T
Source: http://etn.fi/index.php?option=com_content&view=article&id=8034&via=n&datum=2018-05-23_15:17:09&mottagare=31202
Tomi Engdahl says:
Shift spoke of artificial intelligence and self-directed ships
Head of the Future of Humanity Institute at Oxford University Nick Bostrom’s keynote speech attracted yesterday’s ending the end of the thrill of the Turku Shift event. The Visitor’s Office consisted of leaders and influential people from the new technology and the more traditional industry. There was also a share of the application of artificial intelligence to shipping.
Algorithms will never disappear again, but O’Neil wanted to inspire people to ask whether data collected from them and its algorithms combined the outcome on an ethically sustainable basis and whether the information was legally collected? Which bodies control the collection, sharing and merging of information?
Source: https://www.uusiteknologia.fi/2018/05/24/shift-puhui-tekoalysta-ja-itseohjautuvista-laivoista/
Tomi Engdahl says:
System Bits: May 22
AI benefits, disruptions; VR drone testing; wearable smart tech control.
https://semiengineering.com/system-bits-may-22/
AI disruptions and benefits in the workplace
According to Stanford University researchers, artificial intelligence offers both promise and peril as it revolutionizes the workplace, the economy and personal lives.
Visiting scholar James Timbie of the Hoover Institution, who studies artificial intelligence and other technologies, said that in the workplace of tomorrow, many routine jobs now performed by workers will increasingly be assumed by machines, leaving more complicated tasks to humans who see the big picture and possess interpersonal skills. “Artificial intelligence and other advancing technologies promise advances in health, safety and productivity, but large-scale economic disruptions are inevitable.”
Visiting scholar James Timbie says that the artificial intelligence revolution will involve humans and machines working together, with the best results coming from humans supported by intelligent machines.
When it comes to well-paying ‘cognitive’ jobs, many of these will are vulnerable to disruption, perhaps more over time than the well-paying factory jobs that were lost to globalization, Timbie noted. These jobs, which have traditionally been filled by well-educated, well-paid workers include tax preparers, radiologists, paralegals, loan underwriters, insurance adjusters, financial analysts, translators, and even some journalists and software engineers.
Still humans and machines can work together for greater efficiency and productivity, in such as areas as medical diagnosis particularly because a diagnosis is a determination of how information on a patient fits into a pattern characteristic of a disease, which is something machines do well.
Artificial intelligence will both disrupt and benefit the workplace, Stanford scholar says
https://news.stanford.edu/2018/05/17/artificial-intelligence-workplace/
Artificial intelligence offers both promise and peril as it revolutionizes the workplace, the economy and personal lives, says James Timbie of the Hoover Institution, who studies artificial intelligence and other technologies.
Tomi Engdahl says:
http://www.etn.fi/index.php/13-news/8033-mika-lintila-kiina-menee-tekoalyssa-usa-n-ohi
Tomi Engdahl says:
Military brainboxes ponder ‘UK needs you’ list of AI boffins
We’re falling behind, shout Shrivenham sorts
https://www.theregister.co.uk/2018/05/22/military_brainboxes_ponder_uk_needs_you_list_of_ai_boffins/
Rise of the Machines The Ministry of Defence wants to compile a list of AI boffins with UK security clearance that can be hired to help build Britain’s inevitable robotic military future.
The ministry’s latest publication on artificial intelligence and the armed forces, titled Human-Machine Teaming sets out its vision for what the Rise of the Machines could look like in practice.
While it ruled out weaponised AI, if only because of perceived problems with agreeing “a common definition” for lethal autonomous weapon systems*, the “concept note” did set out the MoD’s view of how Britain is falling behind in the race to militarise self-thinking robots.
Tomi Engdahl says:
Deep learning acceleration platform preview released by Microsoft
https://www.vision-systems.com/articles/2018/05/deep-learning-acceleration-platform-preview-released-by-microsoft.html?cmpid=enl_vsd_vsd_newsletter_2018-05-21&pwhid=6b9badc08db25d04d04ee00b499089ffc280910702f8ef99951bdbdad3175f54dcae8b7ad9fa2c1f5697ffa19d05535df56b8dc1e6f75b7b6f6f8c7461ce0b24&eid=289644432&bid=2109585
A preview of Microsoft’s Project Brainwave, a deep learning acceleration platform built with three layers designed for real-time artificial intelligence processing, has been released on the Azure cloud platform and on the edge.
Project Brainwave, according to Microsoft, makes Azure the fastest cloud to run real-time artificial intelligence (AI) and is now fully integrated with Azure Machine Learning. Supporting Intel FPGAs and ResNet50-based neural networks, the hardware is built with three main layers:
A distributed system architecture
A hardware deep neural network (DNN) engine synthesized onto FPGAs
A compiler and runtime for low-friction deployment of trained models
Tomi Engdahl says:
Inverted Pendulum For The Control Enthusiast
https://hackaday.com/2018/05/20/inverted-pendulum-for-the-control-enthusiast/
Once you step into the world of controls, you quickly realize that controlling even simple systems isn’t as easy as applying voltage to a servo. Before you start working on your own bipedal robot or scratch-built drone, though, you might want to get some practice with this intricate field of engineering. A classic problem in this area is the inverted pendulum, and [Philip] has created a great model of this which helps illustrate the basics of controls, with some AI mixed in.
ZIPY : ZIPY Inverted Pendulum Yakshave
A hardware and software balancing cart pole.
https://hackaday.io/project/158289-zipy-zipy-inverted-pendulum-yakshave
Tomi Engdahl says:
Marie Mawad / Bloomberg:
Facebook’s chief AI scientist Yann LeCun says company is designing its own energy-efficient chips to help with analyzing and filtering live video content
technology
Facebook Is Designing Its Own Chips to Help Filter Live Videos
https://www.bloomberg.com/news/articles/2018-05-25/facebook-is-designing-its-own-chips-to-help-filter-live-videos
Tomi Engdahl says:
https://www.wired.com/story/big-techs-fight-for-net-neutrality-moves-behind-the-scenes
Tomi Engdahl says:
Will Intel’s Deep Neural Processor Be Too Late?
https://www.eetimes.com/document.asp?doc_id=1333323
Intel disclosed plans to release late next year its first neural network processor intended for commercial use, but some analysts wonder if it will be too late for Intel to make up ground lost in AI to Nvidia’s Volta GPU architecture.
Tomi Engdahl says:
AI Chip Tests Binary Approach
Imec’s Lenna may explore in-memory compute
https://www.eetimes.com/document.asp?doc_id=1333321
Tomi Engdahl says:
Home> Tools & Learning> Products> Product Review
“AI” is in the air. Is it on your board?
https://www.edn.com/electronics-products/electronic-product-reviews/other/4460695/-AI–is-in-the-air–Is-it-on-your-board-
It seems you can’t swing a Turing Test these days without hitting an “AI”. And while a small number of projects are gunning for the Turing, the vast majority of AI in the air refers to neural networks and image (or other pattern) recognition. In fact, neural networks have pretty much crashed the party, and, for better or worse, are what most people mean by “AI” these days.
AI has been in the cloud for some years now: Voice recognition and machine (e.g., Google) translation accuracy is way up. But what if it’s a sunny day? No cloud. That’s where today’s second-hottest buzzword comes in: The Edge.
Edge computing means that you do lots of crunching, neural networking, or what have you, locally, at the edge of the network. If you still need to call home, the amount of data involved is much less than if you were feeding raw video, say, over the network.
Tomi Engdahl says:
Jared Schroeder / Columbia Journalism Review:
Courts may soon have to decide if AI communicators are entitled to journalistic protections, e.g. in cases when programmer wants to protect sources his bot used — We need to talk about bots. How will the courts address free-expression rights for artificially intelligent communicators?
Are bots entitled to free speech?
https://www.cjr.org/innovations/are-bots-entitled-to-free-speech.php
We need to talk about bots. How will the courts address free-expression rights for artificially intelligent communicators? This conversation is coming, and it may push the Supreme Court to do something it has avoided: define who is and is not a journalist.
For nearly half a century, the US legal system has lived a double life. On the one hand, the Supreme Court has held that journalists do not have greater or lesser rights than other citizens (see Branzburg v. Hayes). On the other, the lower courts have generally ignored or let stand numerous laws or privileges that provide journalists special protections.
Most of these laws and privileges were devised before the Web was publicly available, and the case law is inconsistent in who it applies these protections to online. These journo-specific measures have sometimes been useful tools for citizen publishers—bloggers, message-board posters, social-media commenters—who have faced the same legal difficulties that traditional journalists have for many years: defamation and privacy claims, efforts to compel disclosure of their sources, and so on.
So where do bots come in? Networked technologies have already challenged journalists to distinguish their work from the countless other types of information that flood virtual spaces. Now, non-human entities have the potential to muddy the waters even more. Courts will soon have to explore whether AI communicators have rights as publishers—and whether a bot can be entitled to journalistic protections.
If a bot files FOIA requests, should it be exempt from fees?
While giving free speech to bots sounds shocking, a court decision in favor of an AI entity could benefit news organizations, some of which (the AP and Reuters, among them) have published AI-constructed stories for years. An example is the daily stock-market roundup. Many such stories could be understood as a public good (think news alerts) and thus receive journalistic legal protections—again, if the courts focused on what was published rather than how it was published.
These issues become more complex in the context of fake news and clickbait.
Tomi Engdahl says:
http://www.etn.fi/index.php/13-news/8055-softatalo-nitor-perusti-tekoalytiimin
Tomi Engdahl says:
https://science.slashdot.org/story/18/05/29/2023217/why-thousands-of-ai-researchers-are-boycotting-the-new-nature-journal?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Slashdot%2Fslashdot%2Fto+%28%28Title%29Slashdot+%28rdf%29%29
Tomi Engdahl says:
https://pulse.microsoft.com/fi-fi/business-leadership-fi-fi/na/fa2-tekoalysta-tukialyyn-organisaation-toiminnallisia-ongelmia-ei-kannata-yrittaa-ratkaista-pelkalla-teknologialla/?utm_campaign=businessleadership&utm_medium=content_social_ad24_fifi&utm_content=none_FY18_Q4_BDM&utm_source=facebook
Tomi Engdahl says:
New York Times:
Inside the debate at Google about the company’s cooperation with the military on AI, after contract for Defense Department’s project Maven became public — WASHINGTON — Fei-Fei Li is among the brightest stars in the burgeoning field of artificial intelligence, somehow managing to hold …
https://www.nytimes.com/2018/05/30/technology/google-project-maven-pentagon.html
Tomi Engdahl says:
Robert Hof / SiliconANGLE:
Nvidia unveils HGX-2, a cloud computing platform for AI and high performance computing, which consists of 16 GPUs delivering 2 petaflops per second
Nvidia debuts cloud server platform to unify AI and high-performance computing
https://siliconangle.com/blog/2018/05/30/nvidia-debuts-cloud-server-platform-unify-ai-high-performance-computing/
Tomi Engdahl says:
Stock Market Prediction With Natural Language Machine Learning
https://hackaday.com/2018/05/30/stock-market-prediction-with-natural-language-machine-learning/
Machines – is there anything they can’t learn? 20 years ago, the answer to that question would be very different. However, with modern processing power and deep learning tools, it seems that computers are getting quite nifty in the brainpower department. In that vein, a research group attempted to use machine learning tools to predict stock market performance, based on publicly available earnings documents.
Predicting Stock Performance with Natural Language Deep Learning
https://www.microsoft.com/developerblog/2017/12/04/predicting-stock-performance-deep-learning/?utm_source=twitter&utm_medium=cpc&utm_campaign=twitter-mobile-post-stock-target-open-source-low
We recently worked with a financial services partner to develop a model to predict the future stock market performance of public companies in categories where they invest. The goal was to use select text narrative sections from publicly available earnings release documents to predict and alert their analysts to investment opportunities and risks. We developed a deep learning model using a one-dimensional convolutional neural network (a 1D CNN) based on text extracted from public financial statements from these companies to make these predictions. We used Azure Machine Learning Workbench to explore the data and develop the model. We modeled our solution using the Keras deep learning Python framework with a Theano backend. Our results demonstrate how a deep learning model trained on text in earnings releases and other sources could provide a valuable signal to an investment decision maker.
Tomi Engdahl says:
Does my algorithm have a mental-health problem?
http://bigthink.com/aeon-ideas/does-my-algorithm-have-a-mental-health-problem?utm_campaign=Echobox&utm_medium=Social&utm_source=Facebook#link_time=1526836448
Is my car hallucinating? Is the algorithm that runs the police surveillance system in my city paranoid? Marvin the android in Douglas Adams’s Hitchhikers Guide to the Galaxy had a pain in all the diodes down his left-hand side. Is that how my toaster feels?
This all sounds ludicrous until we realize that our algorithms are increasingly being made in our own image.
Tomi Engdahl says:
https://blog.netprofile.fi/voiko-miljoona-suomalaista-tekoalyasiantuntijaa-olla-vaarassa