AI News, Machine learning and artificial intelligence to aid climate change ... artificial intelligence

Researchers built AI technology that uses algae to fight climate change, and they're planning on releasing the design so anyone can build one

There are only a few ingredients needed for algae to take over: carbon dioxide, light, and water.

The ancient microorganism is thriving thanks to record heat waves and fertilizers washed away into nearby waters.

Although algae can kill marine life, oceanic microalgae also produces about 50% of the oxygen humans breathe.

They recently built an AI-powered machine, the EOS bioreactor, that takes advantage of algae's ability to capture carbon dioxide through photosynthesis.

As the researchers explained, the machine harnesses the natural process of photosynthesis to filter emissions and capture carbon.

The dried algae can then be mixed into animal feed or fertilizer, or can be used as an ingredient in a growing array of consumer products, such as moisturizer and nutritional supplements.

In recent years, steel factories and coal-burning power plants have installed algae-based technology to capture greenhouse gases before they are released into the atmosphere.

our tagline, is about delivering the future that we were promised that we've read about, that we've heard politicians talk about,"

"Problems this size get solved when innovators from around the world can take what they built, can bring those ideas together, and stitch those ideas into a tapestry of solutions that together, collectively, is big enough to meet the challenge,"

So while a technological solution for carbon capture remains an elusive holy grail, these researchers hope to prove that artificial intelligence can help crack the code.

Lamm is hopeful for the future, but said he still wishes people were more focused on the "benefits that society could bring, versus the 'we're all going to be dead in 50 years.'"

NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning

Many in the ML community wish to take action on climate change, yet feel their skills are inapplicable.

This workshop aims to show that in fact the opposite is true: while no silver bullet, ML can be an invaluable tool both in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change.

Climate change is a complex problem, for which action takes many forms - from designing smart electrical grids to tracking deforestation in satellite imagery.

Many of these actions represent high-impact opportunities for real-world change, as well as being interesting problems for ML research.

Mackey (Microsoft Research, Stanford) 8:15 - 8:30 - Welcome and opening remarks 8:30 - 9:05 - Jeff Dean (Google AI) (Invited talk) 9:05 - 9:15 - Felipe Oviedo: Machine learning identifies the most valuable synthesis conditions for next-generation photovoltaics (Spotlight talk) 9:15

- 9:25 - Valentina Zantedeschi: Cumulo: A Dataset for Learning Cloud Classes (Spotlight talk) 9:25 - 9:35 - Qinghu Tang: Fine-Grained Distribution Grid Mapping Using Street View Imagery (Spotlight talk) 9:35 - 9:45 - Shamindra Shrotriya and Niccolo Dalmasso: Predictive Inference of a Wildfire Risk Pipeline in the United States (Spotlight talk) 9:45 - 10:30 - Coffee break + Poster Session 10:30 - 11:05 - Felix Creutzig (MCC Berlin, TU Berlin): Leveraging digitalization for urban solutions in the Anthropocene (Invited talk) 11:05 - 11:15 - Ashish Kapoor: Helping Reduce Environmental Impact of Aviation with Machine Learning (Spotlight talk) 11:15 - 12:00 - Yoshua Bengio, Andrew Ng, Carla Gomes, Lester Mackey, Jeff Dean: Panel - Climate Change: A Grand Challenge for ML 12:00 - 2:00 - Networking lunch (provided) + Poster Session 2:00 - 2:40 - Carla Gomes (Cornell) (Invited talk) 2:40 - 2:50 - Kyle Story: A Global Census of Solar Facilities Using Deep Learning and Remote Sensing (Spotlight talk) 2:50 - 3:00 - Kiwan Maeng: Enhancing Stratospheric Weather Analyses and Forecasts by Deploying Sensors from a Weather Balloon (Spotlight talk) 3:00 - 3:10 - Daisy Zhe Wang: Measuring Impact of Climate Change on Tree Species: analysis of JSDM on FIA data (Spotlight talk) 3:10 - 3:20 - Adrian Albert: Emulating Numeric Hydroclimate Models with Physics-Informed cGANs (Spotlight talk) 3:20 - 3:30 - Draguna Vrabie: Stripping off the implementation complexity of physics-based model predictive control for buildings via deep learning (Spotlight talk) 3:30 - 4:15 - Coffee break + Poster Session 4:15 - 4:40 - Lester Mackey (Microsoft Research) (Invited talk) 4:40 - 4:50 - Saumya Sinha: Detecting Avalanche Deposits using Variational Autoencoder on Sentinel-1 Satellite Imagery (Spotlight talk) 4:50

- 5:00 - Jacob Pettit: Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning (Spotlight talk) 5:00

UCI) Vikram Voleti (Mila, Montreal) Volodymyr Kuleshov (Stanford) Yang Song (Oak Ridge National Lab) Ydo Wexler (Amperon) Zhecheng Wang (Stanford) Zhuangfang Yi (Development Seed) NeurIPS (formerly written “NIPS”) is one of the premier conferences on machine learning, and includes a wide audience of researchers and practitioners in academia, industry, and related fields.

We invite submissions of short papers using machine learning to address problems in climate mitigation, adaptation, or modeling, including but not limited to the following topics: All machine learning techniques are welcome, from kernel methods to deep learning.

Submissions are limited to 3 pages for the Papers track, and 2 pages for the Proposals track, in PDF format (see examples here).

All submissions must explain why the proposed work has (or could have) positive impacts regarding climate change.

Work that is in progress, published, and/or deployed Submissions for the Papers track should describe projects relevant to climate change that involve machine learning.

Submissions should provide experimental or theoretical validation of the method presented, as well as specifying what gap the method fills.

Detailed descriptions of ideas for future work Submissions for the Proposals track should describe detailed ideas for how machine learning can be used to solve climate-relevant problems.

No results need to be demonstrated, but ideas should be justified as extensively as possible, including motivation for why the problem being solved is important in tackling climate change, discussion of why current methods are inadequate, and explanation of the proposed method.

We also encourage workshop participants to apply for NeurIPS 2019 travel grants and other grants (e.g.

If you are aware of additional scholarships that may be relevant to workshop attendees, please contact the workshop organizers so we can make this information available.

A: Feel free to include appendices with additional material (these should be part of the same PDF file as the main submission).

No, the CMT system does not send automatic confirmation emails after a submission, though the submission should show up on the CMT page once submitted.

This policy is as per the NeurIPS workshop guidelines: “Workshops are not a venue for work that has been previously published in other conferences on machine learning or related fields.

Work that is presented at the main NeurIPS conference should not appear in a workshop, including as part of an invited talk… (Presenting work that has been published in other fields is, however, encouraged!)” Q: Can I submit work to this workshop if I am also submitting to another NeurIPS 2019 workshop?

Google's AI Chief Wants to Do More With Less (Data)

Whatever the future role of computers in society, Jeff Dean will have a powerful hand in the outcome.

As the leader of Google’s sprawling artificial intelligence research group, he steers work that contributes to everything from self-driving cars to domestic robots to Google’s juggernaut online ad business.

WIRED talked with Dean in Vancouver at the world’s leading AI conference, NeurIPS, about his team’s latest explorations—and how Google is trying to put ethical limits on them.

After you’ve designed a bunch of new circuitry you have to put it on the chip in an efficient way to optimize for area and power usage and lots of other parameters.

You can have a machine learning model essentially learn to play the game of chip placement, and do so pretty effectively.

JD: There's still a lot of potential to build more efficient and larger scale computing systems, particularly ones tailored for machine learning.

Google announced a set of AI ethics principles 18 months ago, after protests over a Pentagon AI project called Maven.

We have a process by which product teams thinking of using machine learning in some way can get early opinions before they have designed the entire system, like how should you go about collecting data to ensure that it's not biased or things like that.

The Future of Artificial Intelligence

Sitting at his cluttered desk, located near an oft-used ping-pong table and prototypes of drones from his college days suspended overhead, Gyongyosi punches some keys on a laptop to pull up grainy video footage of a forklift driver operating his vehicle in a warehouse.

It was captured from overhead courtesy of a Onetrack.AI “forklift vision system.” Employing machine learning and computer vision for detection and classification of various “safety events,” the shoebox-sized device doesn’t see all, but it sees plenty.

The mere knowledge that one of IFM’s devices is watching, Gyongyosi claims, has had “a huge effect.” “If you think about a camera, it really is the richest sensor available to us today at a very interesting price point,” he says.

Here’s another: Tesla founder and tech titan Elon Musk recently donated $10 million to fund ongoing research at the non-profit research company OpenAI — a mere drop in the proverbial bucket if his $1 billion co-pledge in 2015 is any indication.

This, however, is not: After more than seven decades marked by hoopla and sporadic dormancy during a multi-wave evolutionary period that began with so-called “knowledge engineering,” progressed to model- and algorithm-based machine learning and is increasingly focused on perception, reasoning and generalization, AI has re-taken center stage as never before.

There’s virtually no major industry modern AI — more specifically, “narrow AI,” which performs objective functions using data-trained models and often falls into the categories of deep learning or machine learning — hasn’t already affected.

That’s especially true in the past few years, as data collection and analysis has ramped up considerably thanks to robust IoT connectivity, the proliferation of connected devices and ever-speedier computer processing.

With companies spending nearly $20 billion collective dollars on AI products and services annually, tech giants like Google, Apple, Microsoft and Amazon spending billions to create those products and services, universities making AI a more prominent part of their respective curricula (MIT alone is dropping $1 billion on a new college devoted solely to computing, with an AI focus), and the U.S. Department of Defense upping its AI game, big things are bound to happen.

Of the former, he warned: “The bottom 90 percent, especially the bottom 50 percent of the world in terms of income or education, will be badly hurt with job displacement…The simple question to ask is, ‘How routine is a job?’ And that is how likely [it is] a job will be replaced by AI, because AI can, within the routine task, learn to optimize itself.

And the more quantitative, the more objective the job is—separating things into bins, washing dishes, picking fruits and answering customer service calls—those are very much scripted tasks that are repetitive and routine in nature.

In the matter of five, 10 or 15 years, they will be displaced by AI.” In the warehouses of online giant and AI powerhouse Amazon, which buzz with more than 100,000 robots, picking and packing functions are still performed by humans — but that will change.

“One of the absolute prerequisites for AI to be successful in many [areas] is that we invest tremendously in education to retrain people for new jobs,” says Klara Nahrstedt, a computer science professor at the University of Illinois at Urbana–Champaign and director of the school’s Coordinated Science Laboratory.

In the future, if you don’t know coding, you don’t know programming, it’s only going to get more difficult.” And while many of those who are forced out of jobs by technology will find new ones, Vandegrift says, that won’t happen overnight.

“The transition between jobs going away and new ones [emerging],” Vandegrift says, “is not necessarily as painless as people like to think.”   'In the future, if you don’t know coding, you don’t know programming, it’s only going to get more difficult.” Mike Mendelson, a “learner experience designer” for NVIDIA, is a different kind of educator than Nahrstedt.

While some of these uses, like spam filters or suggested items for online shopping, may seem benign, others can have more serious repercussions and may even pose unprecedented threats to the right to privacy and the right to freedom of expression and information (‘freedom of expression’).

Speaking at London’s Westminster Abbey in late November of 2018, internationally renowned AI expert Stuart Russell joked (or not) about his “formal agreement with journalists that I won’t talk to them unless they agree not to put a Terminator robot in the article.” His quip revealed an obvious contempt for Hollywood representations of far-future AI, which tend toward the overwrought and apocalyptic.

Once we have that capability, you could then query all of human knowledge and it would be able to synthesize and integrate and answer questions that no human being has ever been able to answer because they haven't read and been able to put together and join the dots between things that have remained separate throughout history.” That’s a mouthful.

More than a few leading AI figures subscribe (some more hyperbolically than others) to a nightmare scenario that involves what’s known as “singularity,” whereby superintelligent machines take over and permanently alter human existence through enslavement or eradication.

The late theoretical physicist Stephen Hawking famously postulated that if AI itself begins designing better AI than human programmers, the result could be “machines whose intelligence exceeds ours by more than ours exceeds that of snails.” Elon Musk believes and has for years warned that AGI is humanity’s biggest existential threat.

“I think that maybe five or ten years from now, I’ll have to reevaluate that statement because we’ll have different methods available and different ways to go about these things.” While murderous machines may well remain fodder for fiction, many believe they’ll supplant humans in various ways.

As MIT physics professors and leading AI researcher Max Tegmark put it in a 2018 TED Talk, “The real threat from AI isn’t malice, like in silly Hollywood movies, but competence — AI accomplishing goals that just aren’t aligned with ours.” That’s Laird’s take, too.

“I think that’s science fiction and not the way it’s going to play out.” What Laird worries most about isn’t evil AI, per se, but “evil humans using AI as a sort of false force multiplier” for things like bank robbery and credit card fraud, among many other crimes.

Referencing the rapid transformational effect of nuclear fission (atom splitting) by British physicist Ernest Rutherford in 1917, he added, “It’s very, very hard to predict when these conceptual breakthroughs are going to happen.” But whenever they do, if they do, he emphasized the importance of preparation.

Scientists apply AI to tackle bigger problems, from climate change to food shortages - SiliconANGLE

Scientists are using artificial intelligence and machine learning to study, for example, space weather and its major impacts on Earth’s infrastructure, oceans and their relationship to climate change, as well as global food supply and a way to improve it.

we can train machines to do this on a much grander scale,” said Sebastien de Halleux (pictured, left), chief operating officer of Saildrone Inc., which designs, manufactures and operates a global fleet of wind and solar-powered ocean drones monitoring the state of the planet in real time.

At Bowery Farming, he is building a network of large-scale indoor warehouse farms where it is possible to grow all sort of produce 365 days a year using zero pesticides, hydroponic systems, and LED technology.

“We have eyes on every single crop that grows in our facilities, and so we process those, learn from that data, and funnel that back into the system.” AI and ML are also powerful tools in Saildrone’s data collection platform.

“So we’re trying to use the kind of AI that you use to detect anomalies like people who are trying to break into, to do bank fraud, or do web server attacks.” Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of AWS re:Invent:

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