Posted On:Amazon Archives - AppFerret

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Communicate with Alexa Devices Using Sign Language

2018-07-16 - By 

Many have found Amazon’s Alexa devices to be helpful in their homes, but if you can’t physically speak, it’s a challenge to communicate with these things. So, Abhishek Singh used TensorFlow to train a program to recognize sign language and communicate with Alexa without voice.

Nice.

 


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You can now tell Amazon’s Alexa to make your home smell better

2017-06-20 - By 

Amazon Alexa can already turn off your lights and close your garage. Now it can also make your house smell like a Hawaiian vacation.

Prolitec, which calls itself a “scenting services company,” announced Monday that its Aera fragrance systems can now be voice controlled through the Amazon Echo smart speaker and other Alexa-compatible devices.

The Aera systems offer eight different fragrances, which range from pink grapefruit to basmati rice. The fragrance capsules can operate 24 hours a day and run for a full 60 days.

You can tell Alexa to turn Aera on, have Alexa raise or lower scent levels, or ask what the current scent levels are. If you already own an Aera, you can get it to work with Alexa by enabling the Aera skill in your Amazon Alexa app.

Aera is only the latest attempt to offer smart scents. In the 1950’s Hans Laube invented a “Smell-O-Vision” system for automated odor releases during movies. (Because who wouldn’t want to smell King Kong as he swings through New York?) And for the last 20 years, various companies have experimented with digitized scents that could be embedded in email or web pages.

Original article here.


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The tech industry is dominated by 5 big companies — here’s how each makes its money

2017-05-26 - By 

More and more, everything crucial about the present and future of consumer tech runs through at least one five companies: Alphabet, Apple, Facebook, Amazon, and Microsoft.

Smartphones, laptops, app distribution, voice assistants and AI, streaming music and video, cloud computing, online shopping, advertising — whatever it is, chances are it runs through the oligopoly in some way. The list of startups that have bought by the big five, meanwhile, is almost too long to count.

Each of the five make great products, to be clear, but it’s hard to deny that they control how tech money flows.

How each of those companies make their revenues, though, varies wildly. As this recent chart from Visual Capitalist shows, each of the big five hold their empires on the back of different industries. Google’s parent company Alphabet, for all the dabbling it does, is an online advertising company first and foremost. Facebook is, too. Apple is a hardware company through and through, while everything about Amazon flows from its e-commerce business.

Though it’s still the dominant player in PCs, Microsoft stands out as the only tech giant with diversified sources of revenue. It has Windows, of course, but with the PC market in decline, it’s also getting significant gains from Office, the Azure cloud business, Xbox, Ads, and various other businesses.

Original article here.


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Amazon opens up Alexa’s microphone and voice processing technology to hardware makers

2017-04-14 - By 

Amazon’s digital assistant Alexa might show up in a lot of new devices soon.

That’s because the online retail giant has decided to open up what amounts to Alexa’s ears, her 7-Mic Voice Processing Technology, to third party hardware makers who want to build the digital brain into their devices. The new development kit also includes access to Amazon’s proprietary software for wake word recognition, beamforming, noise reduction, and echo cancellation as well as reference client software for local device control and communication with the Alexa Voice Service.

The move will make it easier and less expensive for hardware makers to build Alexa into their products.

“Since the introduction of Amazon Echo and Echo Dot, device makers have been asking us to provide the technology and tools to enable a far-field Alexa experience for their products,” said Priya Abani, director of Amazon Alexa said in a statement. “With this new reference solution, developers can design products with the same unique 7-mic circular array, beamforming technology, and voice processing software that have made Amazon Echo so popular with customers. It’s never been easier for device makers to integrate Alexa and offer their customers world-class voice experiences.”

Amazon said the new development kit will be invitation only. Device makers can sign up here for an invite and to learn more about the technology.

A similar decision in 2015 to give developers the opportunity to build new capabilities for Alexa through the Alexa Skills Kit helped push Amazon into the early lead in the competitive voice assistant market. Developers who want to add to Alexa’s abilities can write code that works with Alexa in the cloud, letting the smart assistant do the heavy lifting of understanding and deciphering spoken commands.

Alexa reached a milestone of 10,000 skills back in February, and it has surely added many more since. That’s up from 7,000 in January; from 5,400 in December; from 3,000 in September; and from 1,000 in June. That’s a 10X increase since June.

Amazon is battling with Microsoft, Apple, Google, and soon Samsung, in the voice assistant market. Apple and Google opened up their digital assistant platforms to third party developers last year.

Original article here.


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How Does Amazon Alexa Work? (video)

2017-03-14 - By 

In this video, “Hello, Alexa!”, we’re going to introduce the Alexa Skills Kit and teach you how to create skills, which are voice driven applications for Alexa. We will build and deploy a basic skill. This skill will be called the “Greeter” skill, and will say hello to users when they invoke the skill using the words that we specify.

 

Original YouTube video here.


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How to develop an internet of things strategy

2017-02-21 - By 

Former Amazon executive John Rossman shares his checklist for developing an internet of things strategy for your organization.

The internet of things (IoT) may present the biggest opportunity to enterprises since the dawn of the internet age, and perhaps it will be bigger. Research firm Gartner predicts there will be nearly 20 billion devices on the IoT by 2020, and IoT product and service suppliers will generate $300 billion+ in revenue.

Successfully leveraging that opportunity — bringing together sensors, connectivity, cloud storage, processing, analytics and machine learning to transform business models and processes — requires a plan.

“In the course of my career, I’ve estimated and planned hundreds of projects,” John Rossman, who spent four years launching and then running Amazon’s Marketplace business (which represents more than 50 percent of all Amazon units sold today), writes in his new book, The Amazon Way on IoT: 10 Principles for Every Leader from the World’s Leading Internet of Things Strategies. “I’ve learned that, even before you start seeking answers, it’s imperative to understand the questions. Guiding a team to a successful outcome on a complex project requires understanding of the steps and deliverables, necessary resources, and roles and every inherent risk and dependency.”

Before you start the hardware and software design, and before you figure out how to engage developers, he says, you need to start with a better set of questions.

Rossman says there are three key phases to building a successful IoT strategy. While he presents the steps sequentially, he notes that many steps are actually taken concurrently in practice and can be approached in many different ways.

Part 1. Develop and articulate your strategy

First and foremost, Rossman says, you must narrow and prioritize your options. IoT presents a broad swathe of opportunities. Success depends upon understanding your market, evaluating the opportunities with deliberation and attacking in the right place.

Landscape analysis

It all begins with a landscape analysis. You need to thoroughly understand your industry and competitors — strengths, weaknesses, opportunities and threats (SWOT). This will help you see the megatrends and forces at play in your market.

“Creating a landscape analysis and value chain of your industry is a very important thing to do,” Rossman tells CIO.com. “Studying the market: What are they saying about IoT in your industry? Truly understanding what is your worst customer moment: Where do customers get frustrated? What data or what event improves that customer experience? What’s the sensor or IoT opportunity that provides that data?”

Value-chain analysis and profit-pool analysis

The next step, Rossman says, is to create a value-chain analysis and profit-pool analysis of your industry. It should be a broad view of the industry, don’t give in to tunnel-vision with a narrow view of your current business. In some cases, this may involve launching a business in one part of the value chain as a way to gain perspective on the rest of the value chain and to identify other business opportunities.

Partner, competitor and vendor analysis

Create a map of other solutions providers in your space to develop a clear understanding of what exactly each one does, who their key clients are and what their IoT use cases are. Rossman says you should even pick a few to interview. Use this process to understand the needs of customers, the smart way those needs are already being met and where the gaps are.

Customer needs

The next step, Rossman says, is to document specific unmet customer needs and identify the key friction points your future customers are currently experiencing.

“Following the path from start to your desired outcome can help you identify details and priorities that might otherwise be dealt with at too high a level or skipped over entirely,” he writes.

Rossman warns that crafting strong customer personas and journeys is hard work, and you may need to start over several times to get it right.

“The biggest mistake you can make on these is to build them for show rather than for work,” he writes. “Don’t worry about the beauty of these deliverables until things are getting baked (if at all). Do worry about getting at insights, talking to customers and validating your findings with others who can bring insights and challenges to your work.”

Evaluation framework and scoring

Design ways to assess the success of your work.

“This includes understanding a project’s feasibility and transition points and how it will tie into other corporate strategies at your company,” Rossman writes. “Sometimes, especially if your organization is new to the field of connected devices, the success of your project should be measured in terms of what you can learn from the project rather than whether or not it can be classically considered a success.”

You might undertake some early IoT initiatives purely to gain experience, with no expected ROI, he says.

Strategy articulation

Once you have all these analyses under your belt, you need share what you’ve learned with the rest of your team. Rossman says he’s had the most success articulating these learnings by building a flywheel model of business systems and by developing a business model.

Part 2. Build your IoT roadmap

Once you’ve explained your big idea and why your organization should pursue it, you need an IoT roadmap that helps you plan and communicate to others what the journey will be like, what is being built and how it will work.

“In creating your roadmap, embrace on of Amazon’s favorite strategies — think big, but bet small,” Rossman writes.

In other words, you need a big vision, but you don’t want to “bet big.” Make small bets to test your thinking. This can involve creating a prototype, a minimally viable product or jointly developing a project with existing customers and partners.

Rossman suggests four methods that can help you articulate your roadmap:

  • The future press release. Develop a simple but specific product announcement. This forces you to clarify your vision, Rossman says.
  • A FAQ for your IoT plan. Forecast some of the questions you’re likely to get about your product and create a frequently asked questions (FAQ) document to answer them.
  • A user manual. Develop a preliminary user manual for your IoT device. It should address the end user. If the product includes an API, you should also build a user manual for the developer.
  • A project charter. Write a project charter. This is a written project overview that outlines the key facets of the project. It should help you understand the resources you need to undertake the project, what the key milestones are and the schedule.

Part 3. Identify and map your IoT requirements

The last step is to identify and map your IoT requirements — the technical capabilities you need to make your solution a success.

“Companies use many different types of approaches, such as use cases, user stories, process flows, personas, architecture specifications and so on to document their requirements,” Rossman writes.

Regardless of the requirements methodology you settle on, Rossman says it’s important to answer questions around insights (data and events), analytics and recommendations, performance and environment and operating costs.

For example, under ‘insights,’ it’s important to answer questions like these:

  • What problem, event or insight is the end user solving for?
  • What insights would be valuable to the customer?
  • What recommendation or optimization using the data would be valuable to a customer?
  • What data needs to be collected?

Analytics and recommendations questions might include the following:

  • How responsive will “adjustments” or optimizations need to be (specify in time range)?
  • How complex will the “math” be? Write the math equation or pseudologic code if you can.
  • Will notifications, logic, “math,” or algorithms be consistent and fixed, or will they need to be configurable, updated and managed?

Performance questions might include these:

  • Estimate the amount of data transmitted over a period of time (hour, day).
  • What are the consequences of data not being collected?
  • What are the consequences of data being collected but not transmitted?

Environment and operating requirements questions might include these:

  • What operating conditions will the device and sensor be in? Temperature, moisture, pressure, access and vibration are example conditions.
  • What device physical security needs or risks are there?
  • Will the IoT device or sensors be embedded within another device, or will they be independent and a primary physical device themselves?

Costs questions might include these:

  • What is the cost per device target range?
  • What is the cost per device for connectivity target range?
  • What is the additional operating cost range the business can support for ongoing operating infrastructure?

“As you build your plans, remember that though IoT can provide key pieces to the puzzle, it’s no golden ticket,” Rossman writes. “Simply creating an IoT solution will not bring you success. However, if you focus on providing strong value to your customers through new or updated products and services, improving company operations or creating new or more-efficient business models, you’ll be much more likely to find success.”

Original article here.

 


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All the Big Players Are Remaking Themselves Around AI

2017-01-02 - By 

FEI-FEI LI IS a big deal in the world of AI. As the director of the Artificial Intelligence and Vision labs at Stanford University, she oversaw the creation of ImageNet, a vast database of images designed to accelerate the development of AI that can “see.” And, well, it worked, helping to drive the creation of deep learning systems that can recognize objects, animals, people, and even entire scenes in photos—technology that has become commonplace on the world’s biggest photo-sharing sites. Now, Fei-Fei will help run a brand new AI group inside Google, a move that reflects just how aggressively the world’s biggest tech companies are remaking themselves around this breed of artificial intelligence.

Alongside a former Stanford researcher—Jia Li, who more recently ran research for the social networking service Snapchat—the China-born Fei-Fei will lead a team inside Google’s cloud computing operation, building online services that any coder or company can use to build their own AI. This new Cloud Machine Learning Group is the latest example of AI not only re-shaping the technology that Google uses, but also changing how the company organizes and operates its business.

Google is not alone in this rapid re-orientation. Amazon is building a similar group cloud computing group for AI. Facebook and Twitter have created internal groups akin to Google Brain, the team responsible for infusing the search giant’s own tech with AI. And in recent weeks, Microsoft reorganized much of its operation around its existing machine learning work, creating a new AI and research group under executive vice president Harry Shum, who began his career as a computer vision researcher.

Oren Etzioni, CEO of the not-for-profit Allen Institute for Artificial Intelligence, says that these changes are partly about marketing—efforts to ride the AI hype wave. Google, for example, is focusing public attention on Fei-Fei’s new group because that’s good for the company’s cloud computing business. But Etzioni says this is also part of very real shift inside these companies, with AI poised to play an increasingly large role in our future. “This isn’t just window dressing,” he says.

The New Cloud

Fei-Fei’s group is an effort to solidify Google’s position on a new front in the AI wars. The company is challenging rivals like Amazon, Microsoft, and IBM in building cloud computing services specifically designed for artificial intelligence work. This includes services not just for image recognition, but speech recognition, machine-driven translation, natural language understanding, and more.

Cloud computing doesn’t always get the same attention as consumer apps and phones, but it could come to dominate the balance sheet at these giant companies. Even Amazon and Google, known for their consumer-oriented services, believe that cloud computing could eventually become their primary source of revenue. And in the years to come, AI services will play right into the trend, providing tools that allow of a world of businesses to build machine learning services they couldn’t build on their own. Iddo Gino, CEO of RapidAPI, a company that helps businesses use such services, says they have already reached thousands of developers, with image recognition services leading the way.

When it announced Fei-Fei’s appointment last week, Google unveiled new versions of cloud services that offer image and speech recognition as well as machine-driven translation. And the company said it will soon offer a service that allows others to access to vast farms of GPU processors, the chips that are essential to running deep neural networks. This came just weeks after Amazon hired a notable Carnegie Mellon researcher to run its own cloud computing group for AI—and just a day after Microsoft formally unveiled new services for building “chatbots” and announced a deal to provide GPU services to OpenAI, the AI lab established by Tesla founder Elon Musk and Y Combinator president Sam Altman.

The New Microsoft

Even as they move to provide AI to others, these big internet players are looking to significantly accelerate the progress of artificial intelligence across their own organizations. In late September, Microsoft announced the formation of a new group under Shum called the Microsoft AI and Research Group. Shum will oversee more than 5,000 computer scientists and engineers focused on efforts to push AI into the company’s products, including the Bing search engine, the Cortana digital assistant, and Microsoft’s forays into robotics.

The company had already reorganized its research group to move quickly into new technologies into products. With AI, Shum says, the company aims to move even quicker. In recent months, Microsoft pushed its chatbot work out of research and into live products—though not quite successfully. Still, it’s the path from research to product the company hopes to accelerate in the years to come.

“With AI, we don’t really know what the customer expectation is,” Shum says. By moving research closer to the team that actually builds the products, the company believes it can develop a better understanding of how AI can do things customers truly want.

The New Brains

In similar fashion, Google, Facebook, and Twitter have already formed central AI teams designed to spread artificial intelligence throughout their companies. The Google Brain team began as a project inside the Google X lab under another former Stanford computer science professor, Andrew Ng, now chief scientist at Baidu. The team provides well-known services such as image recognition for Google Photos and speech recognition for Android. But it also works with potentially any group at Google, such as the company’s security teams, which are looking for ways to identify security bugs and malware through machine learning.

Facebook, meanwhile, runs its own AI research lab as well as a Brain-like team known as the Applied Machine Learning Group. Its mission is to push AI across the entire family of Facebook products, and according chief technology officer Mike Schroepfer, it’s already working: one in five Facebook engineers now make use of machine learning. Schroepfer calls the tools built by Facebook’s Applied ML group “a big flywheel that has changed everything” inside the company. “When they build a new model or build a new technique, it immediately gets used by thousands of people working on products that serve billions of people,” he says. Twitter has built a similar team, called Cortex, after acquiring several AI startups.

The New Education

The trouble for all of these companies is that finding that talent needed to drive all this AI work can be difficult. Given the deep neural networking has only recently entered the mainstream, only so many Fei-Fei Lis exist to go around. Everyday coders won’t do. Deep neural networking is a very different way of building computer services. Rather than coding software to behave a certain way, engineers coax results from vast amounts of data—more like a coach than a player.

As a result, these big companies are also working to retrain their employees in this new way of doing things. As it revealed last spring, Google is now running internal classes in the art of deep learning, and Facebook offers machine learning instruction to all engineers inside the company alongside a formal program that allows employees to become full-time AI researchers.

Yes, artificial intelligence is all the buzz in the tech industry right now, which can make it feel like a passing fad. But inside Google and Microsoft and Amazon, it’s certainly not. And these companies are intent on pushing it across the rest of the tech world too.

Original article here.

 


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124 HIDDEN AMAZON ALEXA EASTER EGGS EVERY ALEXA USER SHOULD KNOW [INFOGRAPHIC]

2016-12-31 - By 

Amazon’s Alexa is one of the most popular V.A. and connected home hubs out there.

Amazon Echo and Dot, the main Alexa-enabled devices, are so popular, that they are almost impossible to get online. Even Amazon’s spokesperson recommends that if you happen to see one for sale – grab it right away.

There’s no doubt that the secret to Amazon’s device’s success is in its connected-home system, Alexa.

From being the center hub to your Mark Zuckerberg’s-like smart home to simply giving you the right answers when you need them Alexa is one of the best connected-home assistants in the market.

To get the most out of your Alexa device, you can either enable 3rd party skills from the Amazon Alexa store (like News Feed, for example), but you can also use its built in skills that come natively with the device.

But wait, there’s more.

In addition to the popular uses and familiar skills, there are a bunch of hidden invocations and not-so-well-known questions you can ask it – and get great answers on.

We’ve collected 124 Amazon Alexa must-know voice commands that you might not know of.

Some will make you more productive.
Some will make you laugh.
Some will get you playing with your Echo all day long.
In any case, you’ve got a long day ahead of you.

So go ahead and dive into the 124 Alexa commands.

Original article here.


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Google, Facebook, and Microsoft Are Remaking Themselves Around AI

2016-11-24 - By 

FEI-FEI LI IS a big deal in the world of AI. As the director of the Artificial Intelligence and Vision labs at Stanford University, she oversaw the creation of ImageNet, a vast database of images designed to accelerate the development of AI that can “see.” And, well, it worked, helping to drive the creation of deep learning systems that can recognize objects, animals, people, and even entire scenes in photos—technology that has become commonplace on the world’s biggest photo-sharing sites. Now, Fei-Fei will help run a brand new AI group inside Google, a move that reflects just how aggressively the world’s biggest tech companies are remaking themselves around this breed of artificial intelligence.

Alongside a former Stanford researcher—Jia Li, who more recently ran research for the social networking service Snapchat—the China-born Fei-Fei will lead a team inside Google’s cloud computing operation, building online services that any coder or company can use to build their own AI. This new Cloud Machine Learning Group is the latest example of AI not only re-shaping the technology that Google uses, but also changing how the company organizes and operates its business.

Google is not alone in this rapid re-orientation. Amazon is building a similar group cloud computing group for AI. Facebook and Twitter have created internal groups akin to Google Brain, the team responsible for infusing the search giant’s own tech with AI. And in recent weeks, Microsoft reorganized much of its operation around its existing machine learning work, creating a new AI and research group under executive vice president Harry Shum, who began his career as a computer vision researcher.

Oren Etzioni, CEO of the not-for-profit Allen Institute for Artificial Intelligence, says that these changes are partly about marketing—efforts to ride the AI hype wave. Google, for example, is focusing public attention on Fei-Fei’s new group because that’s good for the company’s cloud computing business. But Etzioni says this is also part of very real shift inside these companies, with AI poised to play an increasingly large role in our future. “This isn’t just window dressing,” he says.

The New Cloud

Fei-Fei’s group is an effort to solidify Google’s position on a new front in the AI wars. The company is challenging rivals like Amazon, Microsoft, and IBM in building cloud computing services specifically designed for artificial intelligence work. This includes services not just for image recognition, but speech recognition, machine-driven translation, natural language understanding, and more.

Cloud computing doesn’t always get the same attention as consumer apps and phones, but it could come to dominate the balance sheet at these giant companies. Even Amazon and Google, known for their consumer-oriented services, believe that cloud computing could eventually become their primary source of revenue. And in the years to come, AI services will play right into the trend, providing tools that allow of a world of businesses to build machine learning services they couldn’t build on their own. Iddo Gino, CEO of RapidAPI, a company that helps businesses use such services, says they have already reached thousands of developers, with image recognition services leading the way.

When it announced Fei-Fei’s appointment last week, Google unveiled new versions of cloud services that offer image and speech recognition as well as machine-driven translation. And the company said it will soon offer a service that allows others to access to vast farms of GPU processors, the chips that are essential to running deep neural networks. This came just weeks after Amazon hired a notable Carnegie Mellon researcher to run its own cloud computing group for AI—and just a day after Microsoft formally unveiled new services for building “chatbots” and announced a deal to provide GPU services to OpenAI, the AI lab established by Tesla founder Elon Musk and Y Combinator president Sam Altman.

The New Microsoft

Even as they move to provide AI to others, these big internet players are looking to significantly accelerate the progress of artificial intelligence across their own organizations. In late September, Microsoft announced the formation of a new group under Shum called the Microsoft AI and Research Group. Shum will oversee more than 5,000 computer scientists and engineers focused on efforts to push AI into the company’s products, including the Bing search engine, the Cortana digital assistant, and Microsoft’s forays into robotics.

The company had already reorganized its research group to move quickly into new technologies into products. With AI, Shum says, the company aims to move even quicker. In recent months, Microsoft pushed its chatbot work out of research and into live products—though not quite successfully. Still, it’s the path from research to product the company hopes to accelerate in the years to come.

“With AI, we don’t really know what the customer expectation is,” Shum says. By moving research closer to the team that actually builds the products, the company believes it can develop a better understanding of how AI can do things customers truly want.

The New Brains

In similar fashion, Google, Facebook, and Twitter have already formed central AI teams designed to spread artificial intelligence throughout their companies. The Google Brain team began as a project inside the Google X lab under another former Stanford computer science professor, Andrew Ng, now chief scientist at Baidu. The team provides well-known services such as image recognition for Google Photos and speech recognition for Android. But it also works with potentially any group at Google, such as the company’s security teams, which are looking for ways to identify security bugs and malware through machine learning.

Facebook, meanwhile, runs its own AI research lab as well as a Brain-like team known as the Applied Machine Learning Group. Its mission is to push AI across the entire family of Facebook products, and according chief technology officer Mike Schroepfer, it’s already working: one in five Facebook engineers now make use of machine learning. Schroepfer calls the tools built by Facebook’s Applied ML group “a big flywheel that has changed everything” inside the company. “When they build a new model or build a new technique, it immediately gets used by thousands of people working on products that serve billions of people,” he says. Twitter has built a similar team, called Cortex, after acquiring several AI startups.

The New Education

The trouble for all of these companies is that finding that talent needed to drive all this AI work can be difficult. Given the deep neural networking has only recently entered the mainstream, only so many Fei-Fei Lis exist to go around. Everyday coders won’t do. Deep neural networking is a very different way of building computer services. Rather than coding software to behave a certain way, engineers coax results from vast amounts of data—more like a coach than a player.

As a result, these big companies are also working to retrain their employees in this new way of doing things. As it revealed last spring, Google is now running internal classes in the art of deep learning, and Facebook offers machine learning instruction to all engineers inside the company alongside a formal program that allows employees to become full-time AI researchers.

Yes, artificial intelligence is all the buzz in the tech industry right now, which can make it feel like a passing fad. But inside Google and Microsoft and Amazon, it’s certainly not. And these companies are intent on pushing it across the rest of the tech world too.

Original article here.


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Amazon Echo now talks you through 60,000 recipes

2016-11-21 - By 

Allrecipes’ Alexa skill helps you cook, even if you’re not sure what you want to make.

Believe it or not, there hasn’t really been a comprehensive recipe skill for Amazon Echo speakers. Campbell’s skill is focused on the soup brand, IFTTT integration is imperfect and Jamie Oliver’s skill won’t read cooking instructions aloud. Allrecipes might just save the day, though. It just launched an Alexa skill that guides you through cooking 60,000 meals — and importantly, helps you find something to cook in the first place. You can ask what’s possible with the ingredients you have on hand, find a quick-to-make dish or check on measurements.

When you’re in the middle of cooking, you can pause, repeat or advance steps.

The skill is free to use, and works with any device that supports Alexa skills in the first place (including Fire TV). If it works as well as promised, it might be a crucial addition. The Echo is already the quintessential kitchen speaker for many people — it’s that much more useful if it can save you from flipping through a cookbook (or a recipe app on your phone) with your flour-covered hands.

Original article here.

 


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IBM, Google, Facebook, Microsoft, Amazon form enormous AI partnership

2016-09-29 - By 

On Wednesday, the world learned of a new industry association called the Partnership on Artificial Intelligence, and it includes some of the biggest tech companies in the world. IBM, Google, Facebook, Microsoft, and Amazon have all signed on as marquis members, though the group hopes to expand even further over time. The goal is to create a body that can provide a platform for discussions among stakeholders and work out best practices for the artificial intelligence industry. Not directly mentioned, but easily seen on the horizon, is its place as the primary force lobbying for smarter legislation on AI and related future-tech issues.

Best practices can be boring or important, depending on the context, and in this case they are very, very important. Best practices could provide a framework for accurate safety testing, which will be important as researchers ask people to put more and more of their lives in the hands of AI and AI-driven robots. This sort of effort might also someday work toward a list of inherently dangerous and illegitimate actions or AI “thought” processes. One of its core goals is to produce thought leadership on the ethics of AI development.

So, this could end up being the bureaucracy that produces our earliest laws of robotics, if not the one that enforces them. The world “law” is usually used metaphorically in robotics. But with access to the lobbying power of companies like Google and Microsoft, we should expect the Partnership on AI to wade into discussions of real laws soon enough. For instance, the specifics of regulations governing self-driving car technology could still determine which would-be software standard will hit the market first. With the founding of this group, Google has put itself in a position to perhaps direct that regulation for its own benefit.

But, boy, is that ever not how they want you to see it. The group is putting in a really ostentatious level of effort to assure the world it’s not just a bunch of technology super-corps determining the future of mankind, like some sort of cyber-Bilderberg Group. The group’s website makes it clear that it will have “equal representation for corporate and non-corporate members on the board,” and that it “will share leadership with independent third-parties, including academics, user group advocates, and industry domain experts.”

Well, it’s one thing to say that, and quite another to live it. It remains to be seen if the group will actually comport itself as it will need to if it wants real support from the best minds in open source development. Below, the Elon Musk-associated non-profit research company OpenAI responds to the announcement with a rather passive-aggressive word of encouragement.

The effort to include non-profits and other non-corporate bodies makes perfect sense. There aren’t many areas in software engineering where you can claim to be the definitive authority if you don’t have the public on-board. Microsoft, in particular, is painfully aware of how hard it is to push a proprietary standard without the support of the open-source community. Not only will its own research be stronger and more diverse for incorporating the “crowd,” any recommendations it makes will carry more weight with government and far more weight with the public.

That’s why it’s so notable that some major players are absent from this early roll coll — most notably Apple and Intel. Apple haslong been known to be secretive about its AI research, even to the point of hurting its own competitiveness, while Intel has a history of treating AI as an unwelcome distraction. Neither approach is going to win the day, though there is an argument to be made that by remaining outside the group, Apple can still selfishly consume any insights it releases to the public.

Leaving such questions of business ethics aside, robot ethics remains a pressing problem. Self-driving cars illustrate exactly why, and the classic thought experiment involves a crowded freeway tunnel, with no room to swerve or time to brake. Seeing a crash ahead, your car must decide whether to swerve left and crash you into a pillar, or swerve right and save itself while forcing the car beside you right off the road itself. What is moral, in this situation? Would your answer change if the other car was carrying a family of five?

Right now these questions are purely academic. The formation of groups like this show they might not remain so for long.

Original article here.


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