New Yorker applied machine learning to blocked bike lane problem

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Alex Bell likes to bike around New York City, but he got fed up with how often bike lanes were blocked by delivery trucks and idling cars. So he decided to do something about it, the New York Times reports. Bell is a computer scientist and he develop…
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Microsoft achieves first human-level Chinese to English machine translation using AI

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Microsoft has announced that it has created first machine human-level Chinese to English translation. It is one of a kind Artificial Intelligence (AI)-powered machine system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as humans. Researchers from both USA and Asia said that their system has achieved human parity on a commonly used test set of news stories named newstest2017 which was developed by a group of industry and academic partners and released at a research conference WMT17 last fall. Post the tests, the company says that the results are accurate and on par with human-level. The team said that they have hired external bilingual human evaluators, who compared the Microsoft’s results to two independently produced human reference translations. The company is calling it a major milestone in one of the most challenging natural language processing tasks. The team used the dual-learning method meaning that a sentence which is sent to translation from Chinese to English, was also translated it back from English to Chinese. This method made sure the translation accurate, and it allowed the system to refine and learn from its own mistakes. Xuedong Huang, a technical member in charge of Microsoft’s speech, natural language, and machine translation efforts said: It …
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App Marketers Turn AI and Machine Learning To Drive Growth

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Did you know that 80 percent of users churn within three months of downloading an app? That’s because most apps are marketed to the masses and not necessarily to the right customers.

Oftentimes, the goal of app marketing is to reach as many consumers as possible with the hopes of recruiting en masse and converting at better-than-average ratios. But part of the challenge for marketers is that many of today’s strategies are driven by metrics that don’t link to advanced user targeting and growth.

More specifically, app marketers aren’t using available data strategically to deliver productive user experiences that ultimately drive greater business profitability.

Now more than ever, marketers must shift from tracking traditional vanity metrics to measuring the very things that contribute to retention and growth. More and more, successful companies are investing in customer-centric metrics such as CLV (customer lifetime value) to gain intelligent, consumer-centered insights that not only identify the most valuable customers but also key behaviors and preferences to continually improve consumers’ experiences and journey.

Next-generation marketing and CX are about identifying and engaging valuable consumers

CLV is more important than apps in isolation. It helps apps and other touch points work together to deliver value-added, cohesive experiences.

CLV measures the value a consumer represents to the business across all interactions over their lifetime, not just a single transaction or touch point. That is ultimately the definition of customer experience. It is the sum of all moments a customer has with your brand throughout their life cycle. Marketing and customer engagement is now a cross-functional mandate.

Not all app users are the right users. If you use the Pareto Principle, you can assume that 80 percent of business value is attributed to 20 percent of your active consumers. While these percentages aren’t by any means a standard, they do emphasize the need to identify and cultivate the important customers who drive your business.

Instead of casting a wide net and attracting as many users as possible in the hopes of retaining a reasonably active base, CLV tied to artificial intelligence (AI) and machine learning focuses marketers and also developers on targeted engagement and growth. The idea is to drive profit by investing in more value-added user experiences and personalized offers. Doing so intentionally cultivates meaningful relationships with key customers.

Next-generation customer engagement is about cross-functional collaboration and data sharing

Unfortunately, customer experience today is largely siloed. Marketing, mobile, in-store, e-commerce, digital and so on are not collaborating nor operating against the same customer and market data. But that’s all about to change with the proliferation of AI and machine learning tied to smart CLV initiatives.

When the goal is to deliver targeted and integrated experiences, not just in-app, but across each touch point and the life cycle overall, companies create a truly customer-centric approach. AI then helps brands get a more complete, shared view and understanding of customer behaviors and expectations.

Additionally, AI-driven customer-centricity fosters cross-functional collaboration and data sharing that, by design, boosts customer experiences, along with CLV and business growth.

Identify highest-value customers and deliver targeted experiences

AI/machine learning platforms offer intelligent insights when pointed in the right direction. Successful brands study how much revenue highest-value customers drive over their lifetime and how much it costs to manage those relationships. And they examine CLV across all channels to get a holistic view of high-value behavior in all interactions. When the system can analyze important traits of high-value users, it can learn how to optimize CLV.

For example, to reach potential high-value customers, AI/machine learning uses data from existing high-value customers to optimize campaigns and touch points. In a study by Bain aimed at retail banking, it was found that it costs banks $ 4 every time a customer calls or visits. However, if consumers can complete the transaction via an app, it costs only 10 cents.

The key is to deliver capabilities in ways that consumers prefer and appreciate. Imagine how much AI and machine learning could additionally uncover when tasked with identifying friction points and new opportunities.

AI and CLV call for a new customer-centric playbook

You’ve probably heard time and time again that it costs more to acquire a new customer than to retain one. Brands that are winning prioritize CLV and AI and are drafting the playbook as they go. They:

  • develop a customer-centric mindset.
  • open doors between silos around in-store, digital and mobile so teams can focus on one clear business goal, rather than individual metrics (such as engagement or clicks).
  • align customer-facing groups to a business outcome such as CLV and promote cross-functional collaboration and data sharing to assemble a holistic view of the customer across all touch points.
  • understand who their highest-value customers are, how much revenue they drive over their lifetime and how much it costs to manage the relationship — across all channels.
  • focus on measuring and communicating clear business goals rather than individual or vanity metrics.

AI and machine learning improve both by using existing data without cognitive bias. The more the system learns, the more it optimizes.

In the end, not all customers are created equal. By identifying those who drive value, how and why, you can learn how to design and deliver personalized value to them and enhance customer engagement and experiences to grow your business now and over time.

The post App Marketers Turn AI and Machine Learning To Drive Growth appeared first on ReadWrite.


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Android P feature spotlight: Google improves neural network API for machine learning and AI developers

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Last year, Google introduced a new neural networks API in Android 8.1 Oreo that provided developers with hardware-backed tools for machine learning. Now, with Android P, Google is expanding the API to support nine new operations. Pixel 2 devices will also have support for Qualcomm’s Hexagon HVX driver, giving developers further improvements in performance on those devices. 

At the time, Google’s neural network API supported on-device model creation, compilation, and execution, meaning you could not only build a model as required on the device, but you could also run it.

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Android P feature spotlight: Google improves neural network API for machine learning and AI developers was written by the awesome team at Android Police.

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GreenWaves Technologies joins the race for machine learning at the edge

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The GAP8 processor block diagram for those of y’all who like block diagrams!

A few weeks back, I wrote about the need for machine learning at the edge and what big chip firms are doing to address the challenge. Even as Intel, ARM, and others invest in new architectures, startups are also attempting to innovate with new platforms.

GreenWaves Technologies, based in France, is one such company. It has built a machine learning chip that offers multiple cores and low-power machine learning at the edge. The chip is called the GAP8 application processor. GreenWaves CEO Loic Lietar says the GAP8 processor can offer always-on face detection with a few milliwatts of power, indoor people counting with years of battery life, and sub-$ 15 machine vision or voice control for consumer applications.

But what’s really fascinating about GreenWaves is that it has built this chip with so little in funding. The company has raised 3.1 million euros ($ 3.8 million) so far, much less than a traditional semiconductor startup. And yet it’s gotten all the way to producing chips based on its design, with a development board coming in April. The reason GreenWaves could do so much with so little is because it’s building on a new open-source hardware architecture called RISC-V.

RISC-V was created eight years ago as a project at UC Berkeley aimed at building low-power chips that use minimal instruction sets. An instruction set governs how software talks to the actual computing elements on the chip, and anyone can use it to build their own designs. By comparison, Intel keeps its x86 instruction set to itself (and AMD), while the instruction sets of ARM and MIPS are licensed out for millions of dollars. (For more on this dynamic, here’s a good article.)

GreenWaves’ Lietar says the cost of building a new chip using ARM’s instruction set would start with a $ 15 million license and escalate from there. But with RISC-V his engineers could simply download the code and get going. Of course, to design a chip at this level still requires sophisticated engineers experienced in building processors. However, GreenWave’s founders hail from ST Microelectronics and have lots of chip design experience.

I’m excited by GreenWaves’ chips, but I’m even more excited that in an era when we are going to need different types of processors designed for low-power, edge computing jobs, there’s potentially a way to innovate in silicon at a lower cost. Because in my opinion, silicon is where your innovation starts. The capability has to be there in hardware before you can do new things with software.

Stacey on IoT | Internet of Things news and analysis

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Feds move to secure mobile devices with machine learning, biometrics

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Amid the growing use of mobile devices for work by federal employees, U.S. defense and intelligence agencies are fast adopting biometrics and other alternative ways of  computers, smartphones and tablets, according to a new report.

More than 90% of federal agency IT officials in an online survey said their organizations provide secure mobile access for work-issued devices, but less than 20% support workers’ personal devices to access most agency systems. Forty percent of those same officials voiced concern about securing personal devices, according to the online survey of federal government IT and cybersecurity officials.

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You can take Google’s Machine Learning Crash Course for free now

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Google’s new ‘Machine Learning Crash Course’ is now available and it’s free for everyone. If robots are coming for your job this class will prepare you for your next one. This same course has been taken by more than 18,000 Google engineers, and this is the first time it’s been made available to the general public. It’s part of the company’s ‘Learn With Google AI’ initiative. According to Google it’s free because the world needs to understand AI: We believe that the potential of machine learning is so vast that every technical person should learn machine learning fundamentals. During the…

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This app uses machine learning and AR to teach you how to draw

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For some people drawing comes naturally. But, if you’re anything like me, your stick figures look like misshapen horrors for whom death would surely be a relief. Enter Sketch AR: the AI-powered app that aims to make you a better artist regardless of your current skill. The idea is pretty simple: you draw a few plus-signs on a piece of paper, wall, or sleeping friend’s face and then point your smartphone’s camera at it. From there you can cycle through point-by-point lessons that help you learn to associate free space with the next layer of a drawing. It’s quite intuitive and…

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Apple said to have lured away Twitter, Palm, Kleiner Perkins veteran Michael Abbott with AI and machine learning

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Apple has reportedly recruited Michael Abbott, who has a long history in the tech industry — most notably serving as Twitter’s VP of engineering, and heading up Palm’s webOS team.
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Apple-backed Israel Machine Vision Conference to feature talk on iPhone X’s TrueDepth camera

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Apple is helping to sponsor March’s Israel Machine Vision Conference in Tel Aviv, where the company will also have an expert on hand to talk about the TrueDepth camera on the iPhone X.
AppleInsider – Frontpage News