Artificial intelligence

The Facebook Messenger 2.0 Platform: What Is It And Why Is It Important?

How Samsung And Qualcomm Are Driving Towards Connected Cars

Facebook CEO Mark Zuckerberg. Photo credit: Josh Edelson/AFP/Getty Images)

What is the Facebook Messenger 2.0 platform, and why is it important? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.

Answer by Brian Roemmele, Founder and Editor at Read Multiplex, on Quora:

What is the Facebook Messenger 2.0 platform, and why is it important?

Messenger 2.0 is a huge leap for the platform and for Facebook. It is clear they will give the bot platform a voice. Ultimately, we will likely see Facebook create an Alexa-like Voice First device.

Many assumed the bot was dead already, Facebook made it far more intelligent.

Facebook is already the equivalent to the White Pages directory of a bygone phone era, a 1.2 billion-person modern directory. At the F8 conference, Facebook announced that it is moving to become the Yellow pages and using AI to facilitate the programming to over 60 million businesses on the platform. At last year’s F8 conference, Facebook announced the bot platform and the intelligent agent called M on the Messenger platform. The bot platform attracted many developers and also far more users than most observers have understood. There have been stories of very high engagement and very high sales via the platform. Although the infrastructure for users and developers have been a bit of a disappointment, the concept has proven itself in places like China. With Messenger 2.0, Facebook is moving the platform to be far more useful.

While the quality of bots over the last year has been mixed, a year later these new features could help Facebook, which now has 5 million active advertisers, build a wider, more active user base and “reinvent the way people and businesses are communicating” as Facebook’s VP of messaging David Marcus put it last year. Many tech observers have called bots a failure and assumed Facebook would give up on bots. In fact, it’s making them easier to use and access than ever thanks to elements of Messenger 2.0 and in particular Chat Extensions, which is a way for multiple people in your group chat to chat with the same business at the same time and thereby create a new way for many potential commerce interactions. This is all part of the M Suggestions, the wide release of the Facebook M personal assistant feature that rolled out in Messenger a few of weeks ago.

“We’ve created an ecosystem of developers that are now enabling large companies to do different types of things, whether they want to do brand stuff or whether they want to plug into their huge call centers with thousands of people and allow them to answer Messenger messages instead of phone calls”— David Marcus, VP Facebook

There are many elements of the new Messenger 2.0 platform, but here are the highlights:

Smart Replies:

One of Facebook’s main goals with Messenger last year was to make businesses use the product more to interact with customers. With Smart Replies, this will be an order of magnitude more successful at using Messenger. Smart Replies let businesses with Business Pages use Facebook’s AI bot engine to automatically respond to frequently asked questions such as business hours and contact details. The smart replies API gives businesses the ability to create an AI responder, powered by Facebook’s code. Today the system will do this in a completely automatic way with no programming on the part of the merchant. The AI and Machine Learning (ML) will scan the Business Page and produce an instant and useful response to basic questions.

Over time the ML will become more informed about successful responses and constantly update to deliver the correct or desired result. At this point Smart Replies are limited to basic questions and intents, in the future this will expand to far more complex operations that include commerce. For example, placing a food order at a quick service restaurant would be a next likely avenue that is not much more complex than scanning a FAQ page. I also see this extending into web commerce with the same very high potential for commerce.

Smart Replies will bring millions of small and medium sized businesses into an area that would have cost perhaps thousands of dollars. This solves the chicken/egg problem to populate the Bot platform, overnight millions of businesses can be on the platform making it very useful. Small and medium sized merchants can focus on responding to questions that fall outside of the abilities of the AI and over time the AI will have the answer ready. Today, Facebook is starting off by providing this service…

Why Waymo’s self-driving car test in Phoenix is such a big milestone

Artificial intelligence is not the savior of mankind.

All of the intelligence we program into an interface — a speaker like Amazon Echo, the chatbot you use at work, or a car that drives on its own — has to be tested in the real world by actual human beings, and until those tests are perfected, it won’t be saving anyone. Even then, this computer entity is really an extension of the human mind, isn’t it? An AI is only as smart as the humans who create and program it — nothing more and nothing less.

That’s why autonomous cars are so important. They won’t be the savior of all mankind, but the AI in cars will certainly save a few lives — perhaps even millions.

That’s why the recent news that Waymo (the sister company of Google) is using a fleet of 500 test vehicles — the Chrysler Pacifica minivan, outfitted with LIDAR sensors and other tech to keep the car on the straight and narrow — is so important. No autonomous car will ever reach full production standards at larger automakers like Chrysler and GM until there are real people sitting in the vehicle — including kids, as Google’s Waymo has explained.

Here’s why that is.

Every expert in the auto industry knows there are millions of variables when it comes to driving, and an AI has to collect and analyze all…

Get Hands-on With the Nvidia Jetson TX2 Developer Kit at GTC 2017

AI is hot right now, and Nvidia is leading the prototyping with their new Jetson TX2 board for AI at the edge. Its high-performance, low-power capabilities put machine learning squarely into the hands of makers.

Normally costing $599, Nvidia’s Jetson TX2 Developer Kit will be on sale for $399 at this year’s GPU Technology Conference (GTC), taking place May 8-11 at the San Jose Convention Center. If you are going to GTC and are interested in the kit, there will be three Jetson TX2 focused hands-on labs, each geared towards helping attendees familiarize themselves with different aspects and functions of the board.

GTC is a fabulous opportunity for makers looking to further their understanding of graphics processing units and…

Meet the People Who Train the Robots (to Do Their Own Jobs)

SAN FRANCISCO — What if part of your job became teaching a computer everything you know about doing someone’s job — perhaps your own?

Before the machines become smart enough to replace humans, as some people fear, the machines need teachers. Now, some companies are taking the first steps, deploying artificial intelligence in the workplace and asking their employees to train the A.I. to be more human.

We spoke with five people — a travel agent, a robotics expert, an engineer, a customer-service representative and a scriptwriter, of sorts — who have been put in this remarkable position. More than most, they understand the strengths (and weaknesses) of artificial intelligence and how the technology is changing the nature of work.

Here are their stories.

Rachel Neasham, travel agent

Ms. Neasham, one of 20 (human) agents at the Boston-based travel booking app Lola, knew that the company’s artificial intelligence computer system — its name is Harrison — would eventually take over parts of her job. Still, there was soul-searching when it was decided that Harrison would actually start recommending and booking hotels.

At an employee meeting late last year, the agents debated what it meant to be human, and what a human travel agent could do that a machine couldn’t. While Harrison could comb through dozens of hotel options in a blink, it couldn’t match the expertise of, for example, a human agent with years of experience booking family vacations to Disney World. The human can be more nimble — knowing, for instance, to advise a family that hopes to score an unobstructed photo with the children in front of the Cinderella Castle that they should book a breakfast reservation inside the park, before the gates open.

Ms. Neasham, 30, saw it as a race: Can human agents find new ways to be valuable as quickly as the A.I. improves at handling parts of their job? “It made me feel competitive, that I need to keep up and stay ahead of the A.I.,” Ms. Neasham said. On the other hand, she said, using Harrison to do some things “frees me up to do something creative.”

Ms. Neasham is no ordinary travel agent. When she left the Army after serving as a captain in Iraq and Afghanistan, she wanted to work at a start-up. She joined Lola as one of its first travel agents. Knowing that part of her job was to be a role model, basically, for Harrison, she felt a responsibility for Harrison to become a useful tool.

Founded in 2015 by Paul English, who also started the travel-search site Kayak, Lola was conceived as part automated chat service and part recommendation engine. Underlying it all was a type of artificial intelligence technology called machine learning.

Lola was set up so that agents like Ms. Neasham didn’t interact with the A.I. much, but it was watching and learning from every customer interaction. Over time, Lola discovered that Harrison wasn’t quite ready to take over communication with customers, but it had a knack for making lightning-fast hotel recommendations.

At first, Harrison would recommend hotels based on obvious customer preferences, like brands associated with loyalty programs. But then it started to find preferences that even the customers didn’t realize that they had. Some people, for example, preferred a hotel on the corner of a street versus midblock.

And in a coming software change, Lola will ask lifestyle questions like “Do you use Snapchat?” to glean clues about hotel preferences. Snapchat users tend to be younger and may prefer modern but inexpensive hotels over more established brands like the Ritz-Carlton.

While Harrison may make the reservations, the human agents support customers during the trip. Once the room is booked, the humans, for example, can call the hotel to try to get room upgrades or recommend how to get the most out of a vacation.

“That’s something A.I. can’t do,” Ms. Neasham said.

Diane Kim, interaction designer

Ms. Kim is adamant: Her assistant doesn’t use slang or emoji.

Her assistant, Andrew Ingram, also avoids small talk and doesn’t waste time on topics beside scheduling her meetings, she said.

Ms. Kim isn’t being tyrannical. She just knows her assistant better than most bosses, because she programmed him.

Ms. Kim, 22, works as an A.I. interaction designer at, a New York-based start-up offering an artificial intelligence assistant to help people schedule meetings. pitches clients on the idea that, through A.I., they get the benefits of a human assistant — saving the time and hassle of scheduling a meeting — at a fraction of the price.

It’s Ms. Kim’s job to craft responses for the company’s assistants, who are named Andrew and Amy Ingram, or A.I. for short, that feel natural enough that swapping emails with these computer systems feels no different than emailing with a human assistant.

Ms. Kim’s job — part playwright, part programmer and part linguist — didn’t exist before Alexa, Siri and other A.I. assistants. The job is like a translator of sorts. It is to help humans access the A.I.’s superhuman capabilities like 24/7 availability and infallible memory without getting tripped up by robotic or awkward language.

Even in the narrow parameters of scheduling meetings, it takes a lot of machine learning to break down emails for a computer. For example, setting a meeting for “Wednesday” is different than setting a meeting for “a Wednesday,” as in any Wednesday. breaks down emails to its component parts to understand intent.

The automated response is where Ms. Kim takes over. Her job is to imagine how…

Tech firms searching for way to quickly spot video violence

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(Reuters) — Companies from Singapore to Finland are racing to improve artificial intelligence so software can automatically spot and block videos of grisly murders and mayhem before they go viral on social media.

None, so far, claim to have cracked the problem completely.

A Thai man who broadcast himself killing his 11-month-old daughter in a live video on Facebook this week, was the latest in a string of violent crimes shown live on the social media company. The incidents have prompted questions about how Facebook’s reporting system works and how violent content can be flagged faster.

A dozen or more companies are wrestling with the problem, those in the industry say. Google – which faces similar problems with its YouTube service – and Facebook are working on their own solutions.

Most are focusing on deep learning: a type of artificial intelligence that makes use of computerized neural networks. It is an approach that David Lissmyr, founder of Paris-based image and video analysis company Sightengine, says goes back to efforts in the 1950s to mimic the way neurons work and interact in the brain.

Teaching computers to learn with deep layers of artificial neurons has really only taken off in the past few years, said Matt Zeiler, founder and CEO of New York-based Clarifai, another video analysis company.

It’s only been relatively recently that there has been enough computing power and data available for teaching these systems, enabling “exponential leaps in the accuracy and efficacy of machine learning”, Zeiler said.

Feeding images

The teaching system begins with images fed through the computer’s neural layers, which then…

Astro, an AI Email App, Is Here to Help You Finally Clean Out Your Inbox

Every unwanted promotion/newsletter/coupon that shows up in your inbox is a reminder that you should really get your unread messages under control. But after ending up on dozens of useless mailing lists over the years, it can be hard to know where to start. The creators of Astro understand you’re overwhelmed, and they’ve programmed an algorithm to help.

As Fast Company reports, the new app uses artificial intelligence to anticipate how you’ll respond to the messages flooding your email. If there’s someone you correspond with regularly, for example, Astro will notice and automatically prioritize their emails. If you suddenly stop responding to an email chain, Astro will send you a reminder in a chat bot window, highlighting any…

How Capital One transformed into a tech and AI company

Capital One Eno

From ATMs and mobile wallets to chatbots and robo-advisors, technology relentlessly transforms how we store, transfer, and manage money. Capital One moves at the forefront of this transformation — one of the largest and among the first banking institutions in the U.S. to make serious investments in digital technology and artificial intelligence.

Several major financial services firms have publicly announced ambitious plans for AI, including consumer-facing chatbots. Months later, they still haven’t shipped. Companies that aren’t core technology organizations like Google or Facebook often struggle with getting tech products out the door. Capital One is one of the few exceptions to the rule.

Capital One’s transition to digital kicked off when the company formed an in-house software engineering unit and migrated a significant part of operations to the cloud, foreseeing early on that agility would become a key competitive edge for players in the financial sector. More recently, the Fortune 500 firm infused both backend infrastructure and customer-facing channels with smart doses of machine learning and natural language processing, punctuated in March by an industry-first rollout of a gender-neutral and NLP-capable chatbot named Eno (“One” spelled backwards).

In 2016, the Virginia-based lender became the first financial services company to launch customer account access on Amazon’s Alexa platform, allowing users to check their balances, pay their bills, and engage in a wide range of voice-based interactions. The company also announced it plans to be the first financial services company to launch a similar service on Cortana, Microsoft’s personal assistant software.

Capital One’s latest AI-driven initiative is the chatbot Eno, which markedly shifts the medium from voice to text. Ken Dodelin, VP of digital product management, explained, “Texting is the most widely used feature on the smartphone. Ninety-seven percent of smartphone owners text. So we thought that would be a good place for us to spend some time. And we launched the first natural language SMS chatbot from a U.S. bank.

“Through Eno, folks can chat with us in natural language about their credit card accounts and their checking accounts, and we’re able to have…

The Complete Beginners’ Guide to Artificial Intelligence

Ten years ago, if you mentioned the term “artificial intelligence” in a boardroom there’s a good chance you would have been laughed at. For most people it would bring to mind sentient, sci-fi machines such as 2001: A Space Odyssey’s HAL or Star Trek’s Data.

Today it is one of the hottest buzzwords in business and industry. AI technology is a crucial lynchpin of much of the digital transformation taking place today as organizations position themselves to capitalize on the ever-growing amount of data being generated and collected.

So how has this change come about? Well partly it is due to the Big Data revolution itself. The glut of data has led to intensified research into ways it can be processed, analyzed and acted upon. Machines being far better suited to humans than this work, the focus was on training machines to do this in as “smart” a way as is possible.

This increased interest in research in the field – in academia, industry and among the open source community which sits in the middle – has led to breakthroughs and advances that are showing their potential to generate tremendous change. From healthcare to self-driving cars to predicting the outcome of legal cases, no one is laughing now!



inRead invented by Teads

inRead invented by Teads

What is Artificial Intelligence?

The concept of what defines AI has changed over time, but at the core there has always been the idea of building machines which are capable of thinking like humans.

After all, human beings have proven uniquely capable of interpreting the world around us and using the information we pick up to effect change. If we want to build machines to help us to this more efficiently, then it makes sense to use ourselves as a blueprint.


AI, then, can be thought of as simulating the capacity for abstract, creative, deductive thought – and particularly the ability to learn which this gives rise to – using the digital, binary logic of computers.

Research and development work in AI is split between two branches. One is labelled “applied AI” which uses these principles of simulating human thought to carry out one specific task. The other is known as “generalized AI” – which seeks to develop machine intelligences that can turn their hands to any task, much like a person.

Continued from page 1

Research into applied, specialized AI is already providing breakthroughs in fields of study from quantum physics where it is used to model and predict the behavior of systems comprised of billions of subatomic particles, to medicine where it being used to diagnose patients based on genomic data.

In industry, it is employed in the financial world for uses ranging from fraud…

113 enterprise AI companies you should know

TOPBOTS Enterprise AI
TOPBOTS Enterprise AI

Enterprise companies comprise a $3.4 trillion market worldwide of which an increasingly larger share is being allocated to artificial intelligence technologies.

By our definition, “enterprise” technology companies create tools for workplace roles and functions that a large number of businesses use. For example, Salesforce is the primary enterprise software used by sales professionals in a company. Also known as a type of customer relationship management software, or CRM, it is the system of record for sales professionals to enter in their contacts, progress of leads, and for sales metrics to be tracked. Any company directly selling their products and services would benefit from a CRM.

Plenty of enterprise companies use combinations of automated data science, machine learning, and modern deep learning approaches for tasks like data preparation, predictive analytics, and process automation. Many are well-established players with deep domain expertise and product functionality. Others are hot new startups applying artificial intelligence to new problems. We cover a mix of both.

To help you identify the best tools for your business, we’ve broken up our landscape of enterprise AI solutions into functional categories to match organizational workflows and use cases. Most of these enterprise companies can be classified in multiple categories, but we focused on the primary value add and differentiation for each company.

You’re welcome to re-use the infographic below as long as the content remains unmodified and in full.

TOPBOTS Enterprise AI
TOPBOTS Enterprise AI

Business intelligence (BI)

This function derives intelligence from company data, encompasses the business applications, tools, and workflows that bring together information from all parts of the company to enable smart analysis. From streamlining data preparation like Paxata and Trifacta, to connecting data more effectively from different silos like Tamr and Alation, and even automating reports and generating narratives like Narrative Science and Yseop, enterprise companies are improving BI workflows with artificial intelligence.


Productivity at work is often stunted by a myriad of tiny tasks that consume your attention, i.e. “death by a thousand cuts.” Many productivity tools have emerged to eliminate such tasks, such as the endless back and forth required to schedule meetings. Luckily, many of these productivity tools are virtual scheduling assistants like, FreeBusy, and Clara Labs.

Customer management

Taking care of your customers is no easy task. Enterprise companies have recognized this critical area as ripe for disruption with artificial intelligence. DigitalGenius utilizes AI to sift through your customer service data and automate customer service operations. Inbenta’s AI-powered natural language search enables delivery of self-service support in forums and virtual agents. Luminoso creates visual representations of customer feedback, allowing companies to better understand what consumers want.

HR and talent

With the average tenure of a hire getting shorter, hiring and talent management is arguably one of the most difficult areas for every company to tackle. Where can you find the right candidates and how do you keep hires engaged? Companies like Entelo and Scout work from the top of the funnel to get you the most qualified candidates while others like hiQ Labs utilize public data to warn you of staff attrition risks and enable you to create retention strategies.