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As Halli Labs Is Acquired by Google, Here Are Other AI Startups That Have Attracted Tech Giants

by Loknath Das
written by Loknath Das

As Halli Labs Is Acquired by Google, Here Are Other AI Startups That Have Attracted Tech Giants

HIGHLIGHTS

  • Google acquired Bengaluru-based Halli Labs this week
  • It’s the latest AI-focused startup to get acquired
  • Here’s the list of all such startups that have been acquired

Whether it’s Microsoft trying to help Indian farmers predict when it’s going to rain, Google improving its multi-lingual translation service, Facebook trying to figure out who you should be friends with next, or IBM’s Watson trying to change the health industry, global giants are increasingly betting on AI (artificial intelligence) to expand and perfect their services.

But even as these companies — and many more — race to be the frontrunner in the nascent AI space, many believe that it will take years of efforts to fully realise the potential and reap benefits of how this growing technology will shape the products of today and tomorrow.

In doing so, companies are aggressively looking at talents and startups to bring them on-board — or put their money in — and incorporate their learnings. In fact, dozens of startups focused on AI solutions have been bagged by Apple, Google, Microsoft, Facebook, and Amazon among other companies in the past years.

We’ve a run-down on all the popular AI-focused companies that have been acquired by the giant. As you will notice in the list, these acquisitions only go far as back as five-six years, reaffirming how nascent this space is.

Halli Labs
Google took many by surprise this week when the news got out that it has acquired the Bengaluru-based AI startup. Much about the four-month old startup remains unknown, but we know that some of its founding members came from marketplace Stayzilla. Halli Labs is joining Google’s Next Billion team, which aims to solve the last mile problem by serving people from emerging markets.

Tuplejump
Apple confirmed last year that it had acquired the small Indian machine learning company. Back in the day, Tuplejump offered software services to store, process, and visualise data.

Kitt.ai
China’s conglomerate Baidu acquired Kitt.ai, a startup based out of Seattle that works on chatbots and voice-based applications across several platforms.

Lattice Data
Apple acquired Lattice Data, a startup that uses AI to mine “dark data” — the kind of data that is collected by not used normally for broader purposes earlier this year.

MindMeld
Cisco purchased MindMeld, an AI startup that creates “conversational interfaces” through bots and voice-powered assistants. At the time of the acquisition, MindMeld’s API was being used by over 1,200 companies including Samsung, Google, Intel, and Spotify.

Sonalytic
In March this year, Spotify said it was acquiring Sonalytic, a startup that uses machine learning to offer improved music recommendation.

RealFace
Earlier this year, Apple acquired Israel-based startup RealFace, which developers deep-learning based face authentication technology. Before the acquisition, RealFace was betting big on making passwords redundant.

RavenTech
The Chinese startup that is working on similar capabilities as Amazon’s Alexa was acquired by Baidu earlier this year. The company also has an AI-based app called Flow.

Kaggle
Google acquired the online service Kaggle earlier this year. The company hosted data science and machine learning competitions prior to its acquisition.

Maluuba
Microsoft kickstarted the year by announcing it is buying deep-learning startup Maluuba. The Montreal-based company, Microsoft said, offers “one of the world’s most impressive deep learning research labs for natural language understanding.” Before the startup was acquired, it was working on making advancements toward a more general AI by creating “literate machines that can think, reason and communicate like humans — a vision exactly in line with ours.”

Harvest.ai
Amazon quietly acquired the San Diego-based startup earlier this year. The startup uses machine learning to analyse user behavior around a company’s key intellectual property.

Geometric Intelligence
Uber acquired the New York City-based company late last year, as it worked on expanding its AI efforts. Geometric Intelligence was said to work on solutions that could accurately estimate rider locations and travel times, and also help Uber in its self-driving project.

Masquerade Technologies
Facebook acquired Masquerade Technologies after the startup’s app that offered Snapchat-like selfie filters gained popularity among young customers.

msqrd itunes

Zurich Eye
Before it was acquired by Facebook, the startup enabled machines to “independently navigate in any space.” The startup intended to enable “machines to see.” Much about the company remains unknown.

Nervana Systems
Before it was acquired by Intel, Nervana Systems focused on offering AI solutions that could be integrated directly into the silicon.

Wise.io
General Electric acquired Wise.io late last year. The startup offers machine-learning services to help businesses find patterns in their large trove of data stores.

API.AI
Google bagged the two-year-old startup last year after being impressed by its “fast adoption of the technology from people building conversational interfaces for chatbots, connected cars, and smartphone devices among others.”

Genee
Microsoft acquired the AI-powered scheduling tool Genee last year. The company said at the time it would use Genee’s solution directly in its cloud productivity suite Office 365.

Magic Pony
Twitter acquired the London-based machine learning firm last year. Magic Pony offered tech to improve low-quality pictures and videos on the fly by using machine learning.

SwiftKey
Microsoft acquired SwiftKey, an AI-focused startup that makes popular keyboard app for Android and iOS users. Using machine learning, SwiftKey is able to predict the word you’re typing, and the word which will follow it, helping customers with saving time.

swiftkey

Emotient
Apple acquired Emotient, a software company that specialises in reading users’ facial expressions as they worked at a computer. The startup could then break down the expressions and predict how much attention the user paid to certain kind of content.

Whetlab
The five-person Cambridge-based artificial intelligence startup was acquired by Twitter in 2015. The company was working to make it easier for other firms to “tune” their machine learning efforts.

Perceptio
Before Apple acquired the startup in 2015, it was working on developing AI systems on the device itself without requiring access to cloud.

AlchemyAPI
IBM Watson, the artificial intelligence platform acquired the Denver-based company in 2015. This enabled IBMWatson to get access to a community of over 40,000 AlchemyAPI developers, who were working on systems that “learn and interact naturally.”

Equivio
The startup, acquired by Microsoft, offered text analytics service for legal and compliance. It used machine learning to identify documents that needed legal and compliance needs.

Timeful
Google acquired Timeful, a startup that offered smart scheduling using machine learning. The app would analyse your schedule and also encourage you to achieve your specific goals.

Concept.io
Apple acquired Concept.io in mid-2014, and immediately discontinued the startup’s streaming-content app Swell. The app, before it was taken down, offered personalised news feed, and looked at the users’ behavior on how they consumed it.

Vocal IQ
Before getting acquired by the iPhone-maker in 2015, Vocal IQ was building digital assistants using machine learning techniques to recreate the type of talking computers that appear in your favourite Sci-fi movies.

DeepMind Technologies
In one of the largest acquisition of any AI-based startup, Google acquired DeepMind Technologies in 2014. The London-based AI company specialised in advanced algorithms and systems neuroscience.

Face.com
In one of the earliest AI-focused acquisitions, Facebook acquired Face.com in 2012. The Israeli company offered facial recognition technology.

[“Source-gadgets.ndtv”]

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Blogging

Apple has started blogging to draw attention to its AI work

by Loknath Das
written by Loknath Das

After years of near-silence, Apple is slowly starting to make a bit of noise about its work on artificial intelligence. Last December the iPhone maker shared its first public research paperon the topic; this June it announced new tools to speed up machine learning on the iPhone; and today it started blogging. Sort of.

The company’s new website, titled “Apple Machine Learning Journal,” is a bit grander than a blog. But it looks like it will have the same basic function: keeping readers up to date in a relatively accessible manner. “Here, you can read posts written by Apple engineers about their work using machine learning technologies,” says the opening post, before inviting feedback from researchers, students, and developers.

As the perennial question for bloggers goes, however: what’s the point? What are you trying to achieve? The answer is familiar: Apple wants more attention.

It’s clear that the recent focus on AI in the world of tech hasn’t been kind to the iPhone maker. The company is perceived as lagging behind competitors like Google and Facebook, both in terms of attracting talent and shipping products. Other tech companies regularly publish new and exciting research, which makes headlines and gets researchers excited to work for them. Starting a blog doesn’t do much to counter the tide of new work coming out of somewhere like DeepMind, but it is another small step into public life. Notably, at the bottom of Apple’s new blog, prominently displayed, is a link to the company’s jobs site, encouraging readers to apply now.

What’s most interesting, though, is the blog’s actual content. The first post (actually a re-post of the paper the company published last December, but with simpler language) deals with one of the core weaknesses of Apple’s AI approach: its lack of data.

Much of contemporary AI’s prowess stems from its ability to sieve patterns out of huge stacks of digital information. Companies like Google, Amazon, and Facebook have access to a lot of user data, but Apple, with its philosophy of not snooping on customers — in favor of charging megabucks for hardware — has rather tied its hands in that regard. The first post on its machine learning blog offers a small riposte, describing a method of creating synthetic images that can be used to train facial recognition systems. It’s not ground-breaking, but it’s oddly symbolic of what needs to be Apple’s approach to AI. Probably a blog worth following then.

[Source:-theverge]

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Tech Update

Microsoft Researchers in India, US Embedding AI Into Tiny Devices

by Loknath Das
written by Loknath Das

Microsoft Researchers in India, US Embedding AI Into Tiny Devices

A team of 30 researchers at Microsoft labs in Redmond and Benguluru are busy developing a new class of machine-learning software and tools to embed artificial intelligence (AI) onto bread-crumb size computer processors.

The project is part of a paradigm shift within the technology industry that Microsoft CEO Satya Nadelladescribed recently, by saying that “We’re moving from what is today’s mobile-first, cloud-first world to a new world that is going to be made up of an intelligent cloud and intelligent edge”.

“Creating the intelligent edge is a step toward realising the promise of a world populated with tiny intelligent devices at every turn – embedded in our clothes, scattered around our homes and offices,” said John Roach, a world-renowned expert in AI and machine learning, in a Microsoft blog post.

The researchers imagine all sorts of intelligent devices that could be created, from smart soil-moisture sensors deployed for precision irrigation on remote farms to brain implants that warn users of impending seizures so that they can get to a safe place and call a caregiver.

The computer scientists and software engineers are taking top-down and bottom-up approaches to the challenge of deploying machine-learning models onto resource-constrained devices.

The top-down approach involves developing algorithms that compress machine-learning models trained for the cloud to run efficiently on devices such as the Raspberry Pi 3 and Raspberry Pi Zero.

The intelligent devices are part of the so-called Internet of Things, or IoT, except that these things are intended to be smart, or intelligent, even without an Internet connection.

“The dominant paradigm is that these devices are dumb. They sense their environment and transmit their sensor readings to the cloud where all of the machine learning happens. Unfortunately, this paradigm does not address a number of critical scenarios that we think can transform the world,” said Manik Varma, Senior Researcher with Microsoft Research India and a co-leader of the project.

The researchers are working on systems that can run machine learning algorithms on microcontrollers.

“The intelligent devices are part of the so-called Internet of Things, or IoT, except that these things are intended to be smart, or intelligent, even without an Internet connection,” the blog post added.

The drive to embed AI on tiny devices is part of a broader initiative within Microsoft’s research organisation to envision technologies that could be pervasive a decade from now.

[“Source-gadgets.ndtv”]

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Online Marketing

AI. Machine Learning. What’s the Impact on Digital Marketing Today? [PODCAST]

by Loknath Das
written by Loknath Das

AI. Machine Learning. What’s the Impact on Digital Marketing Today? [PODCAST]

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Artificial intelligence and machine learning have been highly predicted trends in marketing and SEO. But how are they changing the industry now? Read on to find out more about the influence of AI on the marketing world in this Search Engine Nerds episode.

Marc Poirier, CEO and co-founder of Acquisio, joins SEJ’s Brent Csutoras to talk about how artificial intelligence and machine learning impacts online marketing.

Poirier also gives us a primer on how AI is affecting local search, and shares his verdict on the battle between AI and humans in search and marketing.

Artificial Intelligence

We’re starting to see AI integrate itself into a lot of marketing campaigns. Is that the direction we’re headed in?

Marc: I think this is the direction most software companies need to start steering towards over the next five years or so. We have all this data we’re looking at; no matter what field of marketing you’re in, there’s just so much data coming at us now. Being able to leverage that data and make good use of it through machine learning and data mining is something that’s happening today, but it’s going to accelerate over the next few years.

How do you see the transition of machine learning and AI really start to interact with marketing over the next five years?

Marc: There are various models of algorithms that are designed to solve specific problems, and they exist out there. So you can just use some of the work that has been done by academics before and apply it to the problem you’re trying to solve to parse through a ton of data and come out with a better answer really rapidly. I think a lot of companies do that today, but it hasn’t necessarily been labeled as such.

The reality is not so much about the data and having tools to visualize it. That’s great, but what are you going to do with it? How do you analyze it? How do you make decisions based on that data, and how do you automate that decision making process? How do you do that in marketing?

Brent: I think we see that even with small data. You can go in and show somebody Google Analytics at a very small scale and show the data.

Marc: Yeah, exactly.

Brent: And people aren’t really willing to implement that data as it is, let alone start getting into large-scale data collection and analysis. I think one of the big things for marketers is this sense of disconnect because it might not be overly new. It might not be overly complicated at the rudimentary level, but when it comes down to it, if I wanted to do AI, if I wanted to incorporate some of this stuff into my marketing campaigns, there’s a disconnect in the path on how I do that.

When you look at an AI solution versus an expert, do you feel that AI provides an equal or better outcome? Or is it still just a time versus money type of scenario?

AI vs expert: man arm wrestling with robot

Marc: No, it’s a better outcome, but it depends on the problem you’re solving. Some of the things we do are just research, so the outcomes are not better.

What we’ve been doing for five or six years is controlling money, and that’s not something humans can do very well, especially at scale. So we build software to help the small business, but we don’t sell it directly to them. We work with large resellers, companies that have tens of thousands of small businesses they need to serve. They need to make sure that the results are on point, that they’re spending the budgets, and they’re maximizing phone calls and clicks and things like that. This is where technology will do a better job than humans 100% of the time.

Brent: Is this similar to the stock market where you really have no chance if you’re not utilizing these tools? Has it gotten to that point, you feel?

Marc: Yeah, it has. We’re making decisions and making changes on budgets and bids every 30 minutes now across tens of millions of ad groups and hundreds of thousands of accounts. So you can’t imagine humans doing that.

First of all, it’s redundant. It’s error-prone and there’s a lot of accurate decision making going on, a lot of machine learning applied that always tries to hone in on the target. We’re trying to spend a $500 budget, or a $200 budget — it’s very little money, so how do we hit it? How do we get really close to it and not go over? How do we learn about that specific business, about their trend? So there are a few levers there that are really important to model.

I mean you could do it if you have just one account to manage and you apply a lot of time and energy. I’m sure humans can do a pretty good job at it, but you can’t do that at scale for small accounts. It just doesn’t make any sense, and it doesn’t work. It’s not possible to make the right decision all the time. That’s what algorithms and AI do.

What would be the steps and tools to start incorporating AI into paid search? What would be the end-point solution for somebody right now?

Marc: We have a product called Promote where any local business can sign up and we’ll create a local presence for them. We’ll create a Google AdWords account for them and all of the keywords they need, the ads, everything. It’s all automated. Then we’ll assign a small budget and control it really well.

If you look at what Google’s doing, or Marin Software, Kenshoo, Adobe, Ignition One, SearchForce, all the companies in this space, all the bid management solutions have some kind of algorithms or rule engines. So there is technology there to help make the right decisions on large data sets, and they do very well for different situations.

There are products out there in the search world that do that, but they’re limited, typically, to bid management. I think the areas of focus for us are how do we go beyond that in the future and automate more and do a better job at writing ad copy, for example, or testing ad copy.

What is it about AI or machine learning that’s really impacting local search? How does it affect what people are doing locally?

Marc: It revolves a lot around delivering value for small budgets, so it’s very difficult to control. If you have a lot of money to spend on AdWords, you’ll be able to see results, and there’s going to be a lot of waste. But you can’t afford any waste when you have a $200 budget for the month, right? You want to get some clicks. You want to get some calls, so it’s really important that we go out there and get those clicks that will cost the least amount of money. They need to be valid clicks and they need to be relevant.

It’s understanding the auction, understanding the profile for each business — the peaks and valleys, time of day, day of week — to see how the data has flowed historically when we have that. This is something the software will pick up, not only per vertical but also by business. You just gain more certainty if you can have many layers.

So you can say, “Okay, I’m looking at the law services category in general, all of the different attorneys and legal services that are available. Do I see patterns on a day of week basis? Yes. I see some kind of trend where Monday seems to be maybe 20% busier than all the other days.”

Then let’s look at by city for example, or by DMA. Do we see trends there as well? Yeah, larger cities seem to have more of that. Then per business, like for this specific business, if I look back three months, do I see a pattern? Do all these things come together? And does that help me make a better decision? How sure am I that this decision of changing that bid, lowering it by five cents will have an impact in the next 30 minutes?

In a nutshell, it’s a lot of complexities, a lot of data points to look at, but it’s to make that small decision. They’re always small changes that we make, and then validate the next 30 minutes, 60 minutes, 90 minutes. Did I see pick up on impressions? Because when there’s not a lot of click-flow, do I at least see more impression flow hour per hour? Those decisions will be tweaked as the function of the results we observe.

How do you feel about where people spend their money on paid ads right now? Do you feel that Google AdWords is still a major place to spend budget? Are you seeing certain social areas better than others?

Marc: Facebook ads is the place where small businesses are going to start because Facebook is making it easy for anyone to start and they’re making it part of your everyday experience. If you have a business page and you wrote a post, we’ll suggest you boost it for 10 bucks or something. Facebook’s been really good at garnering small span from small advertisers, small SMBs. Most SMBs today have realized that before they graduate to do some paid search investments, they’ll go to Facebook first then introduce Google AdWords.

Brent: Is that just because of the ease of use? It’s just something they can easily engage with, and then once they see some kind of traction, they start looking at other areas to run ads?

Marc: Well, yes, it’s easy and there are many ad products on Facebook. It’s a Swiss army knife of advertising. And there are different ways to build your audience as well, so it’s not only the ad products, it’s how you target. It’s quite complex, despite how easy they make it for a small advertiser to start. They have products like local awareness ads that are that simple. But local awareness helps retailers, restaurants and bars, the food industry in general.

But maybe it’s not that helpful for an emergency plumbing service or the landscaper. When you need a roofer, you’re going to look for it. It’s not going to be an awareness play so it may not be the first place you go for those. Depending on the type of business you are, it may be best to start on Facebook when you’re building local awareness. When it’s something that’s more search-driven intent, search ads would be more appropriate to start with.

Do you feel that you can take learnings from one platform to the other? Or do you feel that they really are individual and need to be viewed as individual?

Marc: We’re not finding any learnings from Facebook can apply to search or vice versa. However, the one thing we’re doing there in terms of bringing things together is budget allocation. How do we devise or change the way our product works to automatically allocate the money correctly across search — AdWords, Bing Ads, Facebook? We didn’t crack that nut yet. This is something that’s really interesting to a lot of companies.

But we’re in the middle of exploring if it does make sense, if it adds value. We’re hearing a lot of “yes”. So we’ll continue that investigation, but the idea of having a marketing budget for advertising online, like a digital advertising budget, and to have one place where you can just sort of put your money in there and trust that the algorithm will do the right thing, seems to be appealing to marketers today.

To listen to this Search Engine Nerds Podcast with Marc Poirier:

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Think you have what it takes to be a Search Engine Nerd? If so, message Loren Baker on Twitter, or email him at loren [at] searchenginejournal.com. You can also email Brent Csutoras at brent [at] alphabrandmedia.com.

Visit our Search Engine Nerds archive to listen to other Search Engine Nerds podcasts!

[“Source-ndtv”]

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Internet

Baidu to Fold Embattled Medical Business Into AI, Search Units

by Rajdeep
written by Rajdeep

Baidu to Fold Embattled Medical Business Into AI, Search Units

Chinese search engine giant Baidu Inc will close some of its medical businesses and fold other health segments into its artificial intelligence and search teams, the firm said in a statement sent to Reuters on Thursday.

Baidu has been refocusing its business strategy after Chinese authorities cracked down last year on medical advertising, once a lucrative business sector for the firm, which led to a steep drop in sales in the latter half of 2016.

The firm, China’s answer to Alphabet Inc’s Google, was at the centre of a national media outcry last year when a student with a fatal cancer blamed Baidu averts for directing him to suspect, ultimately ineffectual treatment.

Regulators responded by slapping curbs on the firm’s healthcare advertising business.

Baidu said parts of the medical team would be integrated into the artificial intelligence and search engine teams.

“Other health business units will be shut down and their staff will be offered other roles internally in line with the demands of the company’s development,” the firm said.

The artificial intelligence teams would look to develop applications that could be used in the medical field. These could include areas such as drug development and testing, gene sequencing and patient diagnosis.

“The most important force that could change the medical field is artificial intelligence”, Baidu CEO Robin Li said.

In January, Baidu appointed former Microsoft Corp executive Qi Lu as chief operating officer, part of the broader push into artificial intelligence as a new driver for growth.

[Source:- gadgets.ndtv]

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Online Marketing

Artificial Intelligence is Here: 5 Brands Implementing AI

by Rajdeep
written by Rajdeep

To most people, artificial intelligence (AI) is represented in terms of robots with human-like similarities. Today’s reality is that of AI powering technologies and driving companies to complete tasks and make things easier. Companies are using AI more than ever, even for tasks that you may not consider.

To understand how AI impacts your business, it’s important to understand exactly what artificial intelligence is. According to Merriam Webster artificial intelligence is, “an area of computer science that deals with giving machines the ability to seem like they have human intelligence.” These machines allow technologies to seem like they have a human element while improving the efficiency and consistency of the company.

Below are five companies that leverage an artificial intelligence system to provide a better user experience for each user.

Google – Machine Learning Algorithm

google-ai

As mentioned in a previous post on the impact of artificial intelligence, Google is using artificial intelligence to help their algorithms understand and learn. Google can use AI to make the results more accurate for each user, which provides a better use experience. Google has always been focused on providing the best result for each user.

Besides using artificial intelligence for their algorithms, Google is using the technology for the self-driving cars too. The goal of self-driving cars will be able to analyze the road ahead of it and make decisions in advance by learning from past experiences. The more experiences the self-driving cars encounter the more it can learn to make sure the people riding in the car are protected.

Lastly, like Siri from Apple, Google uses AI to help power its own personal assistant on smartphones. Google will use the technology to learn about certain locations a person typically goes to or eats at. Google also pulls in information from events and emails to show people the information they might want to see as quickly as possible. Google’s personal assistant over time while learn more about the user to help them find the information they are looking for as quickly as possible.

Rare Carat – Kayak of Diamonds

rare-carat-ai

Rare Carat is a website that calls itself the “Kayak of Diamonds.” Rare Carat uses the IBM Watson technology to compare the price of diamonds across various online retailers. The technology allows users to find diamonds at the right price without feeling like they are paying too much if they happened to go to a traditional brick and mortar jeweler. Besides using the technology to find the best diamond, Rare Carat is looking into more AI options that would walk a user through the entire process, much like what you would find in the store. That AI technology would would act like a representative that would answer questions and concerns while someone is making the purchase.

Under Armor – Personal Fitness Advice

under-armor-ai

Under Armor leveraged IBM Watson to create a health-tracking app call “Record.” Record is an app that will track and analyze workouts, sleep data, and nutrition, while also importing data from other fitness tracking apps. The biggest benefit of the fitness-tracking app is the ability to provide personalized recommendations for nutrition and training advice. By using AI, the app will change the way people can seek fitness advice by basically getting a personal trainer experience without going to the gym.

Wayblazer – Language recognition API

wayblazer-ai

Wayblazer, like many other companies, leverages the IBM Watson technology for the travel industry. Wayblazer focuses on the language recognition API to analyze cues and triggers from the traveler’s search to yield personalized hotel recommendations. The AI technology will allow personalized experiences to each user which can lead encourage them to select the right hotel without extensive research.

Spotify – Finding the best music

spotify-ai

Like other music and video streaming services, Spotify uses AI and deep learning to provide recommendations on similar music that users will enjoy. The application monitors the listening preferences and inserts them into a learning algorithm to make the song recommendations. You can see the deep learning being used with the Discover Weekly playlists that show related music based off of preferences. Also, Spotify has been showing suggested songs based on the type of music a user listens to more frequently. This use of AI may not be as advanced as other companies but it still uses predictive analytics and AI algorithms to learn about each user, enhancing the product.

Artificial Intelligence to the Rescue

Artificial intelligence is already pushing companies in the right direction to creating the best user experience. The tools and technologies that are being driven by AI will only increase. Artificial intelligence can help companies become more efficient and create more technologies to help each individual user.

[Source:- alltop]

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Education

AI Applications In Education

by Loknath Das
written by Loknath Das

AI becoming an increasing part of education industry

AI becoming an increasing part of education industry

PIXABAY

As artificial intelligence becomes an increasing part of our daily lives, it’s no wonder that educational institutions are racing to catch up with the need to develop more talent to keep the engine of AI development running. However, not only is education being transformed as far as science, technology, engineering, and math (STEM) curricula, but the education industry as a whole is being transformed by AI. Increasingly, educational institutions from elementary to higher education as well as adult and professional learning are being transformed by intelligent systems that are helping humans learn better and achieve their learning objectives.

One of the greatest challenges with regards to education is that people learn differently and at different rates. Students go through the education system with differing levels of learning ability and aptitude. Some are more adept at “left brain” thinking with skills for analytical thought, while others are more skilled at “right brain” thinking with creative, literary, and communicative ability. Others are challenged in different ways with physical and mental disabilities, or skill sets that differ from one region of the world to another, facing challenges in re-learning new languages and alphabets.

AI-Enabled Hyperpersonalization

AI systems are being used to tailor and personalize learning for each individual student. Through the power of machine learning based hyperpersonalization, AI systems are being used to develop a custom learning profile of each student and customize the training materials for each student based on their ability, preferred mode of learning, and experience. It is expected that by 2024 upwards of 47% of learning management tools will be enabled by AI capabilities. Rather than requiring teachers to create a single curriculum for all students, educators will have augmented intelligence assistance that provides a wide range of materials leveraging the same core curriculum, but cater to the specific needs of each student. These AI-enabled e-Learning tools will reach over $6 Billion in market size by 2024.

Hyperpersonalized and on demand digital content is also created with the help of AI and machine learning which is changing the way things are done in education. Big textbooks providers now have their information broken down or condensed into smaller study guides, chapter summaries, flashcards, as well as short smart notes for better reading and understanding. Learning is therefore getting paperless with time and soon there will be less or no use of hard copy textbooks for learning. AI systems also have an online interactive interface which aids in feedback from the students to their professors for follow up purposes in areas where they might be struggling or have not yet fully grasped.

In addition to customized materials, AI systems are being used to augment tutoring with personal, conversational education assistants. These autonomous conversational agents can answer questions from students, provide assistance with learning or assignment tasks, and reinforce concepts with additional materials that can help reinforce the curriculum. These intelligent assistants are also enhancing adaptive learning features so that each of the students can learn at their own pace or time frames.

Adoption of voice assistants 

In addition, educators are making increased use of voice assistants in the classroom environment. Voice assistants such as Amazon Alexa, Google Home, Apple Siri, and Microsoft Cortana are giving students a chance to interact with educational material without the interaction of the teacher. These devices can be used at home or similar non-educational environment to provide conversational interaction with teaching material and additional educational assistance.

In the higher education environment, universities and colleges are giving students voice assistants instead of the traditionally printed student handbooks or hard-to-navigate websites for help with a number of campus-related informational needs. Arizona State University gives many of its incoming college freshmen an Amazon Alexa as a way to give them much more timely and focused information about their campus needs. These voice assistants can help answer common questions about campus needs as well as be customized for each student’s particular schedule and courses. This greatly reduces the need for internal support and decreases the cost of wasteful college handbook printing that can quickly get out-of-date. The use of voice assistant systems is very much exciting and interesting to most students and is expected to gain broader adoption in the coming years.

Assisting educators with organizational tasks

In addition to education-oriented duties, teachers are also faced with having to manage the classroom environment and handle various organizational tasks. Educators are often required to handle many non-teaching responsibilities such as essay evaluation, grading of exams, filing necessary paperwork, HR and personnel related issues, ordering and managing classroom materials, booking and managing field trips, responding to parents, assisting with conversation and second-language related issues, dealing with sick or otherwise absent students, and otherwise facilitating the learning environment. Educators often spend up to 50% of their time on non-teaching tasks. AI systems are particularly helpful at managing these back office and task related activities. These AI systems can assist with grading activities and provide personalized responses to students. They can also handle routine and repetitive paperwork, deal with logistics related matters, and other personnel issues. AI systems can even provide a first-line interaction with parents and guardians and give access to resources or provide feedback as needed for routine matters giving teachers more time to focus on things that require a personal touch with the students.

Education administrators are also reaping the benefits of AI with administrative tasks by using intelligent assistants to help with a range of administrative needs including budgeting, student applications and enrollment, course management, educator HR related issues, purchasing and procurement activities, expense management, and facilities management. Using intelligent AI-powered systems can greatly improve the efficiency of many educational institutions, lower their operating costs, give them greater visibility into income and expenses, and improve the overall responsiveness of the educational institutions.

On the higher education side, college admissions officials are looking at using AI systems to improve the fairness and quality of the admissions process. AI systems that are trained in a way that eliminates much of the human bias are starting to be used to provide a credible and fair admission using a given criteria when compared to humans. Recent college admissions scandals have increased oversight and governance on admissions processes, and the use of machine learning systems to provide a more systematic way of handling admissions is proving to be fruitful.

In the not too distant future, you can expect that AI and machine learning will be a core part of all educational experiences. AI is starting to show its benefits and application to a wide range of educational needs, and the hope is that it will greatly improve overall learning outcomes for all.

[“source=forbes”]

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Internet

Google’s AI Push Comes With Plenty of People Problems

by Loknath Das
written by Loknath Das

Google's AI Push Comes With Plenty of People Problems

HIGHLIGHTS

  • Google had declared that AI was more important than fire or electricity
  • Company to employ thousands to catch rogue YouTube posters, bots
  • In the latest quarter alone, Alphabet added 2,009 workers

Google CEO Sundar Pichai recently declared that artificial intelligence fueled by powerful computers was more important to humanity than fire or electricity. And yet the search giant increasingly faces a variety of messy people problems as well.

The company has vowed to employ thousands of human checkers just to catch rogue YouTube posters, Russian bots and other purveyors of unsavoury content. It’s also on a buying spree to find office space for its burgeoning workforce in pricey Silicon Valley.

For a company that built its success on using faceless algorithms to automate many human tasks, this focus on people presents something of a conundrum. Yet it’s also a necessary one as lawmakers ramp up the pressure on Google to deter foreign powers from abusing its platforms and its YouTube unit draws fire for offensive videos, particularly ones aimed at younger audiences.

In the latest quarter alone, Google parent Alphabet added 2,009 workers, for a total of 80,110. Over the last three years, it hired a net 2,245 people per quarter on average. That’s nearly 173 per week, or 25 people per day.

Some of the extra workers this year will be part of Google’s pledge to have 10,000 people across the company snooping out videos and other material that violate the company’s policies – but which computers can’t catch on their own. That program will lead to what Google calls “significant growth ” in personnel.

Google will take on even more workers in the current quarter now that it has closed its $1.1 billion (roughly Rs. 7,000 crores) purchase of part of hardware maker HTC, bringing onboard the 2,000-plus engineers who worked on the Pixel smartphone line.

On Thursday, Pichai spoke bullishly about content-checkers hiring, saying the investments now set the company up to capture growth in the future – in the same call with investors that he touted self-driving vehicles developed by Alphabet’s Waymo unit, which aim to do away with human drivers entirely.

For instance, Pichai said he sees consumers increasingly watching YouTube videos on connected TVs in the living room, a lucrative segment of growth for the digital video advertising that helps power Google’s growth.

After controversies over YouTube stars who made anti-Semitic comments or showed video of someone who had apparently died by suicide, Google has tightened its standards. It has limited which YouTube channels can serve up ads; vowed to manually review every video in its most popular channels for 18-to-34-year-olds; and will pay outside companies to ensure that brands don’t have their ads turn up next to unsuitable videos.

“While there have been some concerns, we’re working really hard to address them and respond strongly,” Pichai said.

Some analysts aren’t so sure. Collin Colburn, an analyst with market researcher Forrester, wonders how much of the recent changes are just window dressing at a company for whom hiring thousands of people amounts to little more than pocket change.

“I wonder if it’s more of a move of optics rather than practicality,” Colburn said, noting Google’s “massive” double-digit revenue growth and cash hoard of $102 billion (roughly Rs. 6.54 lakh crores).

Revenue at Google parent Alphabet rose 24 percent from a year ago to $32.32 billion (roughly Rs. 2.07 lakh crores). After subtracting advertising commissions, revenue was $25.87 billion, exceeding Street forecasts of $25.65 billion. But the company swung to a $3 billion loss from a $5.33 billion profit a year earlier, reflecting the recent federal tax overhaul.

Alphabet shares were down 2.3 percent at $1,141.42 (roughly Rs. 73,600) in after-hours trading.

Google’s growing workforce has the company on a real-estate tear.

It recently opened up offices in Austin, Texas; Ann Arbor, Michigan; Boulder, Colorado; and is planning to open offices in Detroit. It broke ground in November on a huge office building in the heart of London, home to its DeepMind artificial intelligence unit, that will come complete with a rooftop running track.

Near its current headquarters, construction is underway on two futuristic dome-like structures infused with natural light, brimming with solar panels and set to open in late 2019. Google is negotiating with the city of Mountain View to add 10,000 housing units, many of which will likely be home to employees known as “Googlers.”

Pichai said the company intends to hire “thousands of people across the US” this year, build or open five new data centres, and make “significant investments” in nine states.

[“Source-gadgets.ndtv”]

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Internet

Microsoft Unveils Azure Databricks, New AI Tools for Developers

by Loknath Das
written by Loknath Das

Microsoft Unveils Azure Databricks, New AI Tools for Developers

To help increase developers’ productivity and simplify app development, Microsoft has announced new data platform technologies and cross-platform developer tools.

The company launched a new AI-powered platform “Azure Databricks” during an event for developers late Thursday.

Designed in collaboration with the founders of Apache Spark, Azure Databricks analytics platform delivers one-click setup, streamlined workflows and an interactive workspace.

The platform will enable organisations to provide self-service analytics and machine learning over all data with enterprise-grade performance and governance, Microsoft said in a statement.

“With today’s intelligent cloud, emerging technologies like Artificial Intelligence (AI) have the potential to change every facet of how we interact with the world,” said Scott Guthrie, Executive Vice President.

“Developers are in the forefront of shaping that potential. We’re announcing new tools and services that help developers build applications and services for the AI-driven future, using the platforms, languages and collaboration tools they already know and love.”

Microsoft is releasing tools designed to help developers, development teams and data scientists collaborate and work together more efficiently for application development, deployment and management.

The company announced the general availability of a “Visual Studio App Centre”.

Developers can use App Centre to increase productivity and accelerate application lifecycle, freeing them to spend more time on new features and better user experiences.

Developers and data scientists can also develop AI models with all the productivity of Visual Studio, on frameworks and languages.

Microsoft also announced “Azure IoT Edge” that will enable AI, advanced analytics and machine learning for the Internet of Things (IoT) powered devices.

[“Source-gadgets.ndtv”]

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Online Marketing

The Echo Chamber of AI and Online Marketing

by Loknath Das
written by Loknath Das

It seems like there’s a lot of new terminology being thrown around these days by people who may not really know what some of these new words actually mean. When they use these words in an ironic manner without realizing it, the rest of us have a good laugh. It’s kind of along the same lines as Godwin’s Law which states the following:

Godwin’s law (or Godwin’s rule of Hitler analogies) is an Internet adage that asserts that “As an online discussion grows longer, the probability of a comparison involving Hitler approaches 1 – that is, if an online discussion (regardless of topic or scope) goes on long enough, sooner or later someone will compare someone or something to Hitler or his deeds.

As the saying implies, when somebody who isn’t familiar with Godwin’s law compares something to Hitler in an online discussion, everyone else in the discussion who is in on the joke has a good laugh about it and the other side is declared the winner of the debate by default. It’s kind of the same thing when one political side accuses the other of being in an “echo chamber”. The very same people leveling those accusations often refuse to engage in civil discourse about matters they disagree upon, creating their very own echo chamber or “personalized bubble”.

Source: Boston College, Carroll School on Management

Perhaps nowhere is an echo chamber more useful than in online marketing. We’ve talked before about how big databases exist today containing your political affiliation, your likes, dislikes, and pretty much everything about you. Ideally, every single ad you are shown is a reflection of your own echo chamber. Over time, the more that marketers learn about you, the more they will try and reinforce your own beliefs to build rapport with you so they can sell you isht.

In the future, we would expect to see every single online interaction customized, from the welcome message at your online bank to the search results you see on Google. Every single form of automated communication will be customized to your profile. You will always hear “the truth”, and you will only buy from those people who believe in the same “truths” you do. This is the “online marketing echo chamber” that we’re moving towards because it’s the only way we can sell to a country that is so divided – like Cyprus. Let’s take a look at a few startups that are paving the way for a future where every company you engage with online shares the same belief system that you do.

The first thing you need to do before you begin building an echo chamber is to figure out which side the person is on who just landed on your homepage using any number of possible devices, from a smartphone to a smart TV. That’s where a company called Adbrain comes in. Founded in 2012, this San Francisco startup has taken in $16.5 million in funding so far from investors that include Cisco (NASDAQ:CSCO) in order to become “a global leader in resolving customer identity for marketers and their partners across people, places and devices“. In order to understand how their technology works, we’re going to teach you a bit about two forms of online marketing; deterministic targeting and probabilistic targeting.

  • Deterministic targeting – If you are logged into the same Gmail account across multiple devices, it’s probably you. If the same 4 people login to Facebook using the same device consistently over time, it’s probably a device shared by a household. This degree of confidence in this method is high but people get pissed off because it feels like an invasion of privacy.
  • Probabilistic targeting – This is where you use lots of different attributes to identify someone using “anonymized data” like browser type, IP address, operating system, or perhaps even the way they type or use a pointing device.

Adbrain believes that probabilistic targeting using AI is the way forward because it focuses on privacy, something at the forefront of customers’ minds. They’ve developed something called “Identity AI” which goes beyond just figuring out who you are on the devices you use but also starts to paint a picture of the life you live:

AI and Online Marketing

Adbrain – AI and Online Marketing

While it uses probabilistic methods, the AI algorithms are trained using deterministic data so they become better at getting the right answer more often. Adbrain can not only determine the devices you use but also the devices you share within your household because it is able to identify all of the individuals within any given household. It even knows obscure facts like any acquaintances you have that visited a particular auto dealership. Now that marketers know every single thing about you, our next startup comes into play.

Now that you know what bucket to throw your website visitors in, you need to start crafting personalized messages to them which reinforce their own unique echo chambers. Founded in 2012, New York startup Persado has taken in $66 million in funds so far from investors that include American Express (NYSE:AXP), Citigroup (NYSE:C), and Bain Capital. Their latest funding round, a Series C in February of last year, was led by Goldman Sachs (NYSE:GS). Persado is all about using natural language generation to “generate the language that emotionally connects with each consumer at scale”. Sure sounds a whole lot like only saying things to people that they want to hear, right? Haven’t we already been doing this for years? Yes, except this time it’s different. This time, AI is writing the content:

Those email subject lines you see above that dramatically outperformed the originals were actually crafted by AI algorithms. Persado claims that their “cognitive content” outperforms human-crafted messages 100% of the time. They go on to say “in rare instances, variations resulting from brand or legal guidelines, serving methodology, or other outlier variables might affect message performance”. (Even the AI algorithms can’t figure out how to deal with legal’s constantly changing requirements of what’s okay and not okay to say.) In order to better demonstrate how this might work, let’s use Nanalyze as use case.

The content in this article is always going to be same regardless of whether you like it or not. Our readers expect nothing less than the truth without sugar coating. While our articles don’t change, maybe our emails to you would. Let’s say you’re one of our readers who is mainly interested in our articles on robotics. When we send you our weekly newsletter (you should subscribe), we may want to change the title of the email to say something about robotics so that you open it and then read the article. More reads show more ads which help pay our bills and feed our MBAs. That’s an example of how we would use customized language to get you to take a particular action. This is not unlike the technology that Boomtrain is working on, a startup that was recently acquired.

Here’s a real world example of how effective Persado can be when applied to a target audience of 717,394 people subscribed to an email distribution list for an international hotel and restaurant chain:

Notice how the focus there is placed on emotions. Are there certain emotions that are elicited differently based on attributes like gender, culture, race, political affiliation, etc.? There sure are. If that somehow makes you uncomfortable to think about, too bad. There’s money at stake here and the almighty dollar has spoken. Every known variable will be used to determine what’s most likely to get your attention and elicit the appropriate emotions in order to start you off at the top of someone’s sales funnel. The future of AI and online marketing is here – and it’s a big echo chamber for you and your household.

The truth is that companies who prefer to get on with their business and remain apolitical are being dragged into the political debate arena which they want to have no part of. While just ignoring the constant barrage of offended people is one good option, the other option is to start building your online marketing with AI such that it appeals to every unique person. If you then allocate the majority of your marketing spend to online one-to-one interactions, you should be able to leave all your competitors in the dust while they are forced to pick sides and cut their target customer base in half. Now that’s a strategy that even Hitler would agree with.

One firm that allows you to buy shares in startups before they IPO is Motif Investing. You can open a Motif Investing account for free with no deposit required  so you are ready to buy shares of future IPOs before they begin trading.

[“Source-nanalyze”]

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