Valve quietly hides Steam Machine section from its game store after years of neglect

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Valve has gone ahead and quietly hidden a link to its Steam Machines webpage from the Steam store itself, a move indicative of the ill-fated project to push a console-like transition for gaming PCs in the living room. The move, noticed by PC Gamer today, doesn’t mean Steam Machines can no longer be purchased. You can still find the live link here, and you can even purchase the recommended Alienware Alpha Steam Machine from Dell if so choose, as well as the Valve-designed SteamOS controller over on Amazon. But the “hardware” tab on Steam now only shows the Steam Controller, the HTC Vive headset, and Valve’s game streaming service Steam Link.

It’s clear Steam Machines are no longer a priority for Valve, which couldn’t overcome the product…

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Google’s Emoji Scavenger Hunt is a browser-based game that tries to make machine learning fun and accessible

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“Machine learning” and “neural network” are familiar terms to anyone who follows what Google is up to these days, but they may not be the most accessible or comprehensible concepts for the masses. And that’s fine — you don’t need to have a firm grasp of machine learning to enjoy better photos or keyboards, for instance. Still, Google has been quietly showcasing ways for users to get more hands-on with these concepts, and the latest such experiment is a game called Emoji Scavenger Hunt.

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Ford vending machine begins dispensing cars in China

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It is no longer enough simply to test-drive a vehicle by riding around the block while a salesperson gives you their well-rehearsed patter. Now, there needs to be some sort of theater around the purchase, or else how will you trick yourself into thin…
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How to use machine learning for your startup’s product

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There’s a misconception that to leverage machine learning you need to be a mathematical genius. In reality, most machine-learning applications use well-understood, well-tested, off-the-shelf algorithms. For many developers, especially those at startups, the real challenge lies in training the data. Overcoming this challenge takes clever product development with an eye on user experience. Do you really need machine learning? Machine learning can make a good product even better: more engaging, more responsive, and more effective. But, before tackling machine learning, ask yourself whether algorithms are right for your product. Start testing the learning aspect with humans before jumping into machine…

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Group protects rainforest with recycled phones, machine learning

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Rainforest Connection, a San Francisco-based non-profit organisation, is using recycled phones and Google’s open source machine learning framework, TensorFlow, to protect the rainforest.

The group has created a low-cost network that listens for sounds of illegal deforestation, and analyses the data.

According to Topher White, founder and CEO of Rainforest Connection, destruction of forests accounts for a rise of nearly 20 percent in greenhouse gas emissions every year. Tropical deforestation has been accelerated by rampant logging, 90 percent of which is done illegally and undercover.

Protecting the world’s rainforests may be the fastest and cheapest way to slow climate change, believes White, and locals and indigenous tribes are best suited to protect critical regions.

“Rainforest Connection is a group of engineers and developers focused on building technology to help locals – like the Tembé tribe from central Amazon – protect their land, and in the process, protect the rest of us from the effects of climate change,” said White in a blog post.

“Chief Naldo Tembé reached out to me a couple years ago seeking to collaborate on ways that technology could help stop illegal loggers from destroying their land. Together, we embarked on an ambitious plan to address this issue using recycled cellphones and machine learning.”

White’s team has built what it believes is the world’s first scalable, real-time detection and alert system for logging and environmental conservation.

The team has hidden modified smartphones powered by solar panels – dubbed ‘Guardian’ devices’ – in trees across threatened areas. The phones monitor the sounds of the forest, and send the audio to cloud-based servers over the local cellphone network.

This is where machine learning steps in. TensorFlow is used to analyse all of the audio data in real-time, listening out for chainsaws, logging trucks, and other evidence of illegal activity. The audio is constantly being sent to the cloud from every phone, 24 hours a day.

White said that the stakes of missing illegal activity are high. “That’s why we’ve come to use TensorFlow, due to its ability to analyse every layer of our data-heavy detection process.

“The versatility of the machine learning framework empowers us to use a wide range of AI techniques with deep learning on one unified platform. This allows us to tweak our audio inputs and improve detection quality.

“Without the help of machine learning, this process would be impossible. When fighting deforestation, every improvement can mean one more saved tree,” he explained.

Read: Bee robotic: Walmart files patents on automating agriculture 

Internet of Business says

This brilliant scheme mixes low-cost, but sophisticated technology – recycled phones – and cloud-based smart analytics. The environmental benefits are twofold: not only does the scheme help protect the rainforest, but it also keeps the phones out of landfills and uses both their processing power and their network connections.

The use of audio, rather than video is smart too: less data, and zero reliance on light and visibility in dense areas of foliage, especially if illegal activities take place at night.

A brilliant, low-cost, connected scheme that gathers data, gets smarter, and has obvious benefits for human beings: a model for IoT developments of every kind.

Read more: How IoT, smart supply chains can avert global food crisis

Read more: Dell takes a fresh look at IoT with Aerofarms

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Rainforest Connection enlists machine learning to listen for loggers and jaguars in the Amazon

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The vastness that makes the Amazon rainforest so diverse and fertile also makes it extremely difficult to protect. Rainforest Connection is a project started back in 2014 that used solar-powered second-hand phones as listening stations that could alert authorities to sounds of illegal logging. And applying machine learning has supercharged the network’s capabilities.

The original idea is still in play: modern smartphones are powerful and versatile tools, and work well as wireless sound detectors. But as founder Topher White explained in an interview, the approach is limited to what you can get the phones to detect.

Originally, he said, the phones just listened for certain harmonics indicating, for example, a chainsaw. But bringing machine learning into the mix wrings much more out of the audio stream.

“Now we’re talking about detecting species, gunshots, voices, things that are more subtle,” he said. “And these models can improve over time. We can go back into years of recordings to figure out what patterns we can pull out of this. We’re turning this into a big data problem.”

White said he realized early on that the phones couldn’t do that kind of calculation, though — even if their efficiency-focused CPUs could do it, the effort would probably drain the battery. So he began working with Google’s TensorFlow platform to perform the training and integration of new data in the cloud.

Google also helped produce a nice little documentary about one situation where Guardians could help native populations deter loggers and poachers:

That’s in the Amazon, obviously, but Rainforest Connection has also set up stations in Cameroon and Sumatra, with others on the way.

Machine learning models are particularly good at finding patterns in noisy data that sound logical but defy easy identification through other means.

For instance, White said, “We should be able to detect animals that don’t make sounds. Jaguars might not always be vocalizing, but the animals around them are, birds and things.” The presence of a big cat then, might be easier to detect by listening for alarmed bird calls than for its near-silent movement through the forest.

The listening stations can be placed as far as 25 kilometers (about 15 miles) from the nearest cell tower. And because a device can detect chainsaws a kilometer away and some species half a kilometer away, it’s not like they need to be on every tree.

But, as you may know, the Amazon is rather a big forest. He wants more people to get involved, especially students. White partnered with Google to launch a pilot program where kids can build their own “Guardian,” as the augmented phone kits are called. When I talked with him it was moments before one such workshop in LA.

Topher White and students at one of the Guardian building workshops.

“We’ve already done three schools and I think a couple hundred students, plus three more in about half an hour,” he told me. “And all these devices will be deployed in the Amazon over the next three weeks. On Earth day they’ll be able to see them, and download the app to stream the sounds. It’s to show these kids that what they do can have an immediate effect.”

“An important part is making it inclusive, proving these things can be built by anyone in the world, and showing how anyone can access the data and do something cool with it. You don’t need to be a data scientist to do it,” he continued.

Getting more people involved is the key to the project, and to that end Rainforest Connection is working on a few new tricks. One is an app you’ll be able to download this summer “where people can put their phone on their windowsill and get alerts when there’s a species in the back yard.”

The other is a more public API; currently only partners like companies and researchers can access it. But with a little help, all the streams from the many online Guardians will be available for anyone to listen to, monitor and analyze. But that’s all contingent on having money.

“If we want to keep this program going, we need to find some funding,” White said. “We’re looking at grants and at corporate sponsorship — it’s a great way to get kids involved too, in both technology and ecology.”

Donations help, but partnerships with hardware makers and local businesses are more valuable. Want to join up? You can get at Rainforest Connection here.

Mobile – TechCrunch

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Apple & IBM to empower businesses with mobile machine learning

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Apple IBM partnership

New mobile machine learning capabilities are coming to Apple devices, thanks to IBM Watson and Apple Core ML.

Under CEO Tim Cook’s leadership, Apple has been angling for an ever-bigger piece of the enterprise pie. Last year we saw the technology giant partner with GE to bring the industrial predictive and analytics capabilities of the Predix IIoT platform to Apple’s iOS.

But for the past few years Apple has been deepening and extending its ties with IBM too. When the two companies announced their strategic partnership in 2014, Tim Cook and IBM CEO Virginia Rometty claimed that “Apple and IBM are like puzzle pieces that fit perfectly together.”

The agreement saw IBM transfer over 150 of their enterprise and IT apps and tools to Apple platforms natively, and IBM selling iPhones and iPads to its business clients the world over. It crucially gave Apple access to the business verticals that Microsoft has historically dominated.

Now the duo has added machine learning to the partnership, combining IBM Watson with Apple Core ML to bring new AI insights to the business apps on Apple devices.

Watson + mobile machine learning

Earlier this week we reported on IBM’s new Watson Assistant AI. The latest announcement with Apple looks to further Watson’s capabilities. The initiative will benefit a class of products numbering in the hundreds, sourced by the collaboration so far – across finance, insurance, energy, manufacturing, aviation and beyond.

In leveraging the new technology, customers can build machine learning models using IBM Watson (the companys’ cloud-based AI platform for business) and train it with their own industry-specific data. This includes the ability to create different machine learning models, compare the results, and run automated experiments – identifying patterns and gaining insights to reach decisions more quickly.

Machine learning is implemented with IBM Watson’s visual modelling tools, such as PixieDust and Brunel but there’s support for Jupyter notebooks with Python, R and Scala – plus the open-source RStudio. This is then converted to Apple’s Core ML to integrate it with Apple-compatible applications.

One such application of machine learning is enabling iPhone cameras to access Watson’s image recognition capabilities. Users can identify and classify content, before analysing it to extract detailed information. This capability could shake up workflows in the industrial, logistics, and healthcare sectors.

The machine learning algorithm will mature over time as apps feed data back to Watson in the cloud. Mahmoud Naghshineh, general manager for IBM Partnerships and Alliances explained, via TechCrunch:

That’s the beauty of this combination. As you run the application, it’s real time and you don’t need to be connected to Watson, but as you classify different parts [on the device], that data gets collected and when you’re connected to Watson on a lower [bandwidth] interaction basis, you can feed it back to train your machine learning model and make it even better.

Internet of Business says

By placing these sorts of automated, intelligent applications in the hands of enterprise workers, via their iPhone or iPad, IBM and Apple are enabling a more informed and mobile workforce. This has the potential to boost efficiency levels, collaboration, and decision-making.

While these two disparate companies may seem like unlikely bedfellows, there was truth in the ‘puzzle piece’ analogy. In merging the design and UX pedigree that come’s with Apple’s decades of consumer experience, with the IT expertise and vertical user-base of IBM, there are the makings of a force to breach fortress Microsoft in the business arena.

The post Apple & IBM to empower businesses with mobile machine learning appeared first on Internet of Business.

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The AI and machine learning innovations taking John Deere to the next level of precision agriculture

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Plenty of companies are talking about artificial intelligence and machine learning today in vague, disconnected terms. It will certainly influence our strategy; not sure how, but everything’s coming up AI, right?

As a pleasant antidote to all that bluff and bluster, how about this from John Stone, senior vice president of the Intelligent Solutions Group at agricultural manufacturing giant John Deere? “AI and machine learning is going to be as core to John Deere as an engine and transmission is.”

Make no mistake about it, these are certainly exciting times for the 180-year-old Deere & Company. The company has in the past several months acquired Blue River Technology, a machine learning-centric startup, as well as opened up a lab in the heart of Silicon Valley.

Yet this is just the way things have been done for some time at the company – it’s just the technology has changed with it.

Than Hartsock, director of precision agriculture solutions at John Deere, has been involved with the company for much longer than his almost 17-year tenure, having grown up on a commercial grain farm in Ohio. In the late 1990s, his education – Hartsock has degrees in soil and crop science – involved working on projects around soil sensing technologies. Deere acquired NavCom Technology, a provider of global navigation satellite system (GNSS) technology, at around the same time. “It was clear, even when I was in high school, that John Deere was uniquely committed to precision agriculture,” says Hartsock.

It was the Internet of Things long before anyone came up with a proper name for it. Yet this initial investment translates to a serious advantage for the company today. “Those early investments have allowed us to, I would say, position the integration of those components into our equipment into our machines, across machines, and into our dealerships,” explains Hartsock. “It went from ‘okay, this is something Deere is doing [and] it may not be completely clear why we’re doing it’, [and] now it’s at the forefront of our company. It’s how we think about our value proposition to the industry, to farmers, crop producers, and customers.”

No stone is left unturned, no crop is left unfurled – and this is where Blue River comes in. The company provides what it calls ‘see and spray’ technology, which utilises machine learning to process, in real-time, images of weeds and crops and tell the sprayer what and where to spray. It makes for a vast improvement on anything a human can do – but it remains important to keep human expertise.

“Farmers, and their advisors and contractors – these are individuals that bring decades and generations of knowledge about the practices, about the land that they farm,” says Hartsock. “The way we see it is the technology – even artificial intelligence and machine learning – provides them the tools to essentially extend and scale their knowledge.

“Imagine the smart spraying scenario… you could imagine an agronomist, a farmer needing to come into that field ahead of time,” Hartsock adds. “What’s the state of the crop? How much input do I want to invest in this crop at this stage? The machine is going to be able to discern between weeds and crops, but I need to decide economically, agronomically, how much I want to invest.”

Hartsock will be speaking at IoT Tech Expo Global in London on April 18-19, discussing how agriculture has become a prime example of optimising on connected technologies. Inside the industry technological advancement has never been clearer – but what about outside it?

Take self-driving cars as an example. You can’t move for hype and headlines around them, but what can they actually do today? Compared to a smart tractor, one can argue it’s mostly child’s play – and Hartsock wants to make clear how smarter machines and the IoT have ‘infiltrated’ agriculture.

“When you look at a planter and a tractor, in many cases, nearly all cases, that planter or that seeder will have a sensor on every row that’s measuring every seed and every row that’s dropped into the soil,” says Hartsock. “It will have a sensor that measures the motion of the planter row unit to make sure the row unit is keeping in close contact with the soil, and if it’s not maintaining contact, the sensor informs an actuator to apply more pressure to the row unit.

“That’s just the planter,” he adds. “The tractor is equipped with many sensors around the engine and transmission, and then that tractor, like most of our large ag machines, is equipped with a 4G modem that then provides connectivity between those sensors and data that’s being acquired, and then connected to the cloud.

“Once the data gets to the cloud we give the user, the farmer, the contractor, the authority over the data to dictate control and share with other partners and other companies,” Hartsock says. “You really then have this ecosystem that evolves, develops, for usage of the data… all generated out of the work that’s being done in the field by that smart machine.”

Than Hartsock will be speaking at IoT Tech Expo Global, in London on 18-19 April. Find out more about the event here.

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Rainforest Connection is fighting deforestation with old phones and machine learning

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Machine learning is pretty cool—in fact, the only computer science course I took in college was related to it. So we tend to get excited by novel uses for the technology here at Android Police, and in a recent post to Google’s blog, a company called Rainforest Connection came up with an interesting application: Deforestation. 

The specific implementation here is ingenious. By chaining together a system of solar-powered recycled Android phones, each device is able to record ambient audio and upload it online via existing cellular networks.

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Apple, IBM partnership expands with new machine learning integrations

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Apple and IBM late Monday announced an expansion to their existing partnership that will allow customers to roll out advanced in-app machine learning capabilities through Apple’s Core ML and IBM’s Watson technology.
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