Deep Learning at Google

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Growing use of deep learning at Google

In 2012, there was not much use of deep neural nets at Google. Over time, we built tools that other teams can pick up and use to solve their problems. The tools were general purpose and were easily repurposed to different domains just by training on different types of data.

What is Deep Learning?– A powerful class of machine learning model
– Modern reincarnation of artificial neural networks

– Collection of simple, trainable mathematical functions

They are loosely based on what we know about the brain

Commonalities with real brains:

-each neuron is connected to a small subset of other neurons

-based on what it sees, it decides what it wants to say

-neurons learn to cooperate to accomplish the task

Each neuron implements a relatively simple mathematical function.

 

HT: Intuition Machine, Mining Business Data

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CB Insights: 100 Most Promising AI Startups

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Source: CB Insights

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Internet of Things (IoT) Forecasts

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See 7 Trends of IoT in 2017

Juniper Research forecasts that the number of connected IoT (Internet of Things) devices, sensors and actuators will reach over 46 billion in 2021. This 200% increase, from 2016, will be driven in large part by a reduction in the unit costs of hardware. Juniper forecasts that it will average close to the ‘magic’ $1 throughout the period. It was found that industrial and public services will post the highest growth over the forecast period, averaging over 24% annually.

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The iPhone 10th Anniversary

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Ten years ago today, Apple announced the iPhone.

Demonstrating the new pocket communicating computer at Macworld in San Francisco, Steve Jobs said:

Every once in a while, a revolutionary product comes along that changes everything… today, we’re introducing three revolutionary products… The first one is a widescreen iPod with touch controls. The second is a revolutionary mobile phone. And the third is a breakthrough Internet communications device…. An iPod, a phone, and an Internet communicator…These are not three separate devices, this is one device, and we are calling it iPhone.

Walter Isaacson in Steve Jobs:

The iPhone was immediately dubbed the “Jesus Phone” by bloggers. But Apple’s competitors emphasized that, at $500, it cost too much to be successful. “It’s the most expensive phone in the world,” Microsoft’s Steve Ballmer said in a CNBC interview. “And it doesn’t appeal to business customers because it doesn’t have a keyboard.”… By the end of 2010, Apple has sold ninety million iPhones and it reaped more than half the total profits generated in the global cell phone market.

From designer Tony Fadell interview with the BBC:

The press mocked the cultish manner in which iPhone was unveiled. Steve Ballmer, at the time Microsoft’s chief executive, famously laughed at the device, calling it “not a very good email machine” that wouldn’t appeal to business users.

“We all laughed at him,” Fadell remembered.

“We also laughed at Blackberry. Whenever I create a new product , and I learned this with Steve [Jobs], if the incumbents laugh at you and the press laugh at you, you go, ‘we’ve hit a nerve’.”

Since that day, more than a billion iPhones have been sold, helping make Apple the richest company in the world.

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In 2017, 2.35 billion people, more than half of the world’s mobile phone users, will regularly use a smartphone, according to eMarketer. And by 2020, smartphones will account for more than 60.0% of mobile phone users worldwide.

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Big Data Success: Decrease Expenses, Create New Revenue Streams

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NewVantage Partners 2017 Big Data Survey:

A strong plurality of executives, 48.4%, report that their firms have realized measurable benefits as a result of Big Data initiatives.  A remarkable 80.7% of executives characterize their Big Data investments as successful, with 21% of executives declaring Big Data to have been disruptive or transformational for their firm.

Focus areas such as efforts to decrease expenses through operational cost efficiencies have proven to be successful (49.2%) for many firms. Efforts to establish a data-driven culture remain more aspirational at this stage, with only 27.9% reporting success. Executives report that efforts to create new avenues for innovation and disruption have had the highest success rate – 64.5% started, 44.3% reporting results, 68.7% success rate.

 

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Pressed Data: Best of 2016

 

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In Pressed Data, my Forbes.com column, I try to chronicle the evolution of digital technologies, their business impact, and the people behind the innovations, business models, and new ideas. In 2016, I covered artificial intelligence—the 60-year-old new new thing, big data—the most recent hottest trend and a catalyst for the new-found popularity of the new one, the fading away of former tech leaders, a number of startups, and a number of influential business and tech innovators. These were the highlights:

When Artificial Intelligence Started To ‘Change The World’

A review of ENIAC in Action: Making and Remaking the Modern Computer, “a nuanced, engaging and thoroughly researched account of the early days of computers, the people who built and operated them, and their old and new applications,” contrasting it with “history as hype, offering a distorted view of the past, sometimes through the tinted lenses of contemporary fads and preoccupations.”

A New Documentary Reveals A One-Dimensional Face Of Big Data

Hype is on full display in “The Human Face of Big Data” of which I wrote: “…in our technology-obsessed world, new technologies and new technology applications tend sometimes to become buzzwords that are hyped, celebrated and often discussed irresponsibly by technology vendors and the media. Unfortunately, ‘The Human Face of Big Data’ by and large falls into this trap, the fascination (self-delusion?) with the idea of we are living a momentous time in history thanks to technology.”

Top 10 Hot Big Data Technologies

The hype—and an ambiguous and ill-defined term—does not mean that there is no value in adopting and applying the set of technologies that can be classified as big data technologies. In TechRadar: Big Data, Q1 2016, Forrester Research evaluated the maturity and trajectory of 22 technologies across the entire data life cycle.

Why Yahoo Lost And Google Won

Many of the Yahoo obituaries published in 2016 contrasted its demise with the flourishing of Google, another Web pioneer. Why was Google’s attempt to “organize all the world’s information” vastly more successful than Yahoo’s? The short answer: Because Google did not organize the world’s information. Google got the true spirit of the Web, as it was invented by Tim Berners-Lee. Following the latter’s disdain for pre-defined classification systems and taxonomies, Google’s founders built their information retrieval business on tracking closely cross-references (i.e., links between pages) as they were happening and correlating relevance with quantity of cross-references (i.e., popularity of pages as judged by how many other pages linked to them). In contrast, Yahoo had a “Chief Ontologist” on staff. As happens often in the cutting-edge technology business, new ideas are “revolutionary” only in the sense of revolving back to old ones: The concept of cross-references can be trace back to Ephraim Chambers’ Cyclopaedia, published in London in 1728.

The 3 Mindset Shifts You Need For A Successful Digital Transformation

Keri Gohman, Executive Vice President and Head of Small Business Banking at Capital One, on what businesses need to do to gain customer loyalty in the new digital environment.

AI And Machine Learning Take Center Stage At Intel Analytics Summit

From the most recent edition of the tech bible: Moore’s Law begat faster processing and cheap storage which begat machine learning and big data which begat deep learning and today’s AI Spring.

­­­Future Business Leaders As Data Scientists: Reflections On The Career Of Intel Chief Data Scientist

A profile of Bob Rogers, Chief Data Scientist for Big Data Solutions at Intel, the entrepreneurial data scientist who has successfully applied artificial intelligence to healthcare.

A Very Short History Of Artificial Intelligence (AI)

Milestones in the evolution of “thinking machines.”

Artificial Intelligence Pioneers: Peter Norvig, Google

A profile of Peter Norvig, Director of Research at Google.

Deep Learning Is Still A No-Show In Gartner 2016 Hype Cycle For Emerging Technologies

In 2016, Gartner has moved machine learning back a few notches from where it placed it on the previous year’s “Hype Cycle,” putting it at the peak of inflated expectations, and still estimating 2 to 5 years until mainstream adoption. Is machine learning an “emerging technology” and is there a better term to describe what most of the hype is about nowadays in tech circles?

Internet Of Things By The Numbers: What New Surveys Found

Things are looking up for the Internet of Things. 80% of organizations have a more positive view of IoT today compared to a year ago, according to a survey of 512 IT and business executives by CompTIA.

A Very Short History Of EMC Corporation

Milestones in the remarkable 37-year history of EMC Corporation, 16 of which I had the pleasure to witness in person.

Only Humans Need Apply Is A Must-Read On AI For Facebook Executives

In Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, knowledge work and analytics expert Tom Davenport and Julia Kirby, a contributing editor for the Harvard Business Review, re-introduce the concept of augmentation to our discussion of the impact of AI on jobs—humans and computers combing “their strengths to achieve more favorable outcomes than either could do alone.”

12 Observations About Artificial Intelligence From The O’Reilly AI Conference

At the inaugural O’Reilly AI conference, 66 artificial intelligence practitioners and researchers from 39 organizations presented the current state-of-AI: From chatbots and deep learning to self-driving cars and emotion recognition to automating jobs and obstacles to AI progress to saving lives and new business opportunities.

Artificial intelligence (AI) And The Future Of Marketing: 6 Observations From Inbound 2016

At Inbound 2016, HubSpot’s co-founders Brian Halligan and Dharmesh Shah entertained 19,000 attendees with their take on the past and future of marketing.

2017 Predictions For AI, Big Data, IoT, Cybersecurity, And Jobs From Senior Tech Executives

Artificial intelligence (and machine/deep learning) is the hottest trend, eclipsing, but building on, the accumulated hype for the previous “new big thing,” big data. The new catalyst for the data explosion is the Internet of Things, bringing with it new cybersecurity vulnerabilities. The rapid fluctuations in the relative temperature of these trends also create new dislocations and opportunities in the tech job market.

Here’s to a productive and enjoyable 2017!

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Artificial Intelligence and the Future of Marketing

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Brian Halligan and Dharmesh Shah at Inbound 2016

At Inbound 2016, HubSpot’s co-founders Brian Halligan and Dharmesh Shah entertained 19,000 attendees with their take on the past and future of marketing. Here’s what I learned from their keynote presentation and a brief interview.

2017 will be the year of the bot. So predicts Halligan, adding “in five years, you will do a lot less navigating through apps and more just asking questions and chatting back and forth with bots… the next thing you know, we like it and it’s easier and more efficient than waiting for the sales rep to call you back.” Shah notes that businesses started building websites in the 1990s so they can answer customer questions 24/7. “Soon,” he says, “they will start building bots. They won’t replace the websites, but they will power them. The shortest time between a customer question and the answer will be a bot. It’s not human vs. bot, it’s human to the bot powered.” (HubSpot’s recent contribution to the bot power movement: Growthbot).

The “marketing conversation” will become a human-machine conversation. That the essence of marketing is a “conversation” between a business (or any “brand”) and its customers and potential customers has been a marketing tenet (and cliché) for a long time. While that conversation has been conducted over the last twenty years increasingly through a computer screen with the help of a keyboard, it is now transforming into human-machine conversation. “The conversational UI,” says Shah, “is going to be an even bigger leap in software than we had with the shift to Web-based software. We are all re-thinking now how to build products.” It’s the most natural way to engage, interact, market and sell: “We will have voice input because it’s much more efficient [than typing] and visual output because it’s more efficient than listening—we can see and read and scan much faster that we can listen. I don’t think screens are going away but the keyboard is likely going to be less and less prevalent.”

AI will accelerate marketing and sales. “In the next few years,” says Shah, we are going to have autonomous, self-driving, marketing automation.” Machine learning will improve sales and marketing software by giving it “the ability to do things without us explicitly telling it what to do.” As a result, tasks such as predictive lead scoring, content recommendations, and email acquisition will get a lot better. Another interesting example Shah pointed to is what he calls ”Match.com for leads”—Automatically routing leads to the right sales person based on analysis of the data about the lead and about the sales people.

Marketers will not be replaced by AI and will be able to skip the boring stuff. “Anything that seems rote or mechanical,” says Shah, “there is no reason for humans to do—it’s all going to go to AI.” Marketers will continue to be involved, however, with anything that has to do with creativity and they will focus on “understanding the customer, figuring out what the overall positioning is, having actual conversations with other humans. More interaction design is what marketers will do rather than the mechanics of marketing.” Bots working in the background as virtual assistants will help with the kind of work we (especially sales people) don’t like to do, such as updating the CRM.

Algorithm development will become a commodity and data will become the key differentiator. Now that you can buy algorithms off-the-shelf, “mere mortals like me don’t have to learn about machine learning per se,” says Shah. “More companies, including HubSpot, will start doing things that we thought required 100 PhDs. The winners will be the ones that have the data that can feed the machine learning algorithm.”

The Link Graph is going to be replaced by the Engagement Graph. Google has gained fame and fortune because it has built the best link graph, indexing and mapping the connections between all Web pages, determining content quality by popularity, i.e., inbound links.  Amazon has built the Product Graph and today, more than 50% of people looking for a product, first turn to Amazon. Facebook has built the Social Graph linking 1.79 billion people, and they use its search box 2 billion times a day. But the future belongs to the Engagement Graph where the quality of content is determined by the number of people listening, interacting, getting engaged.

Ten years ago, Halligan and Shah took the idea of inbound links and applied it to sales and marketing. Giving birth to “inbound marketing” and to HubSpot, they understood that the Web changed how people buy. In this new customer landscape, blanketing the market with generic ads and messages and press releases was not going to work as it did before. Instead, businesses, especially small businesses, should get potential buyers to want to come to them, to find them just like they find a good and relevant Web page. With high quality, helpful, and engaging content, they can gain the buyer’s trust and loyalty.

In his keynote presentation, Halligan enumerated what has changed over the last ten years since the inception of inbound marketing and HubSpot:

2006    2016
Fight for an inch on a 4-foot shelf     Battle for a millimeter on an infinite shelf
Buyers read all day     Buyers watch video all day
Google helps you find answers     Google gives you the answer
Pay per click     Pay per lead
The website augments the salesperson     The salesperson augments the website
Buyer expects to get value after purchase     Buyer expects to get value before purchase

Ten years ago, Halligan and Shah imagined not only the soul of the new marketing—the new type of content you need to produce to get the buyers’ attention—but also the new food for the soul—data. To succeed with inbound marketing, you need to have all the data at your fingertips, the data about what people do on your website, and the data about these people and the needs and desires they represent. “We are a data play,” says Halligan. “The fun part of our job is to try and predict the future and build a platform that will match that future.”

Originally published on Forbes.com

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