Daily Archives: June 7, 2017

Executive Summary

Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics end up paralyzed. So how can companies tell if they are really ready for AI and other advanced technologies? First, managers should ask themselves if they have automated processes in problem areas that cost significant money and slow down operations. Next, managers should ensure they have structured analytics as well as centralize data processes so that the way data is collected is standardized and can be entered only once. After these standard structured analytics are in place, they can integrated with artificial intelligence.

Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them.

By contrast, companies with strong basic analytics such as sales data and market trends make breakthroughs in complex and critical areas after layering in artificial intelligence. For example, one telecommunications company we worked with can now predict with 75 times more accuracy whether its customers are about to bolt using machine learning. But the company could only achieve this because it had already automated the processes that made it possible to contact customers quickly and understood their preferences by using more standard analytical techniques.

So how can companies tell if they are really ready for AI and other advanced technologies?

First, managers should ask themselves if they have automated processes in problem areas that cost significant money and slow down operations. Companies need to automate repetitive processes involving substantial amounts of data especially in areas where intelligence from analytics or speed would be an advantage. Without automating such data feeds first, companies will discover their new AI systems are reaching the wrong conclusions because they are analyzing out-of-date data. For example, online retailers can adjust product prices daily because they have automated the collection of competitors prices. But those that still manually check what rivals are charging can require as much as a week to gather the same information. As a result, as one retailer discovered, they can end up with price adjustments perpetually running behind the competition even if they introduce AI because their data is obsolete.

Without basic automation, strategic visions of solving complex problems at the touch of a button remain elusive. Take fund managers. While the profession is a great candidate for artificial intelligence, many managers spend several weeks manually pulling together data and checking for human errors introduced through reams of excel spreadsheets. This makes them far from ready for artificial intelligence to predict the next risk to client investment portfolios or to model alternative scenarios in real-time.

Meanwhile, companies that automate basic data manipulation processes can be proactive. With automated pricing engines, insurers and banks can roll out new offers as fast as online competitors. One traditional insurer, for instance, shifted from updating its quotes every several days to every 15 minutes by simply automating the processes that collect benchmark pricing data. A utility company made its service more competitive by offering customized, real-time pricing and special deals based on automated smart meter readings instead of semi-annual in-person visits to homes.

Once processes critical to achieving an efficiency or goal are automated, managers need to develop structured analytics as well as centralize data processes so that the way data is collected is standardized and can be entered only once.

With more centralized information architectures, all systems refer back to the primary source of truth, updates propagate to the entire system, and decisions reflect a single view of a customer or issue. A set of structured analytics provides retail category managers, for instance, with a complete picture of historic customer data; shows them which products were popular with which customers; what sold where; which products customers switched between; and to which they remained loyal.

Armed with this information, managers can then allocate products better, and, see why choices are made. By understanding the drivers behind customer decisions, managers can also have much richer conversations about category management with their suppliers such as explaining that very similar products will be removed to make space for more unique alternatives.

After these standard structured analytics are integrated with artificial intelligence, its possible to comprehensively predict, explain, and prescribe customer behavior. In the earlier telecommunications company example, managers understood customer characteristics. But they needed artificial intelligence to analyze the wide set of data collected to predict if customers were at risk of leaving. After machine learning techniques identified the customers who presented a churn risk, managers then went back to their structured analytics to determine the best way to keep them and use automated processes to get an appropriate retention offer out fast.

Artificial intelligence systems make a huge difference when unstructured data such as social media, call center notes, images, or open-ended surveys are also required to reach a judgment. The reason Amazon, for instance, can recommend products to people before they even know they want them is because, using machine learning techniques, it can now layer in unstructured data on top of its strong, centralized collection of structured analytics like customers payment details, addresses, and product histories.

AI also helps with decisions not based on historic performance. Retailers with strong structured analytics in place can figure out how best to distribute products based on how they are selling. But it takes machine learning techniques to predict how products not yet available for sale will do partly because no structured data is available.

Finally, artificial intelligence systems can make more accurate forecasts based on disparate data sets. Fund managers with a strong base of automated and structured data analytics are predicting with greater accuracy how stocks will perform by applying AI to data sets involving everything from weather data to counting cars in different locations to analyzing supply chains. Some data pioneers are even starting to figure out if companies will gain or lose ground using artificial intelligence systems analyses of consumer sentiment data from unrelated social media feeds.

Companies are just beginning to discover the many different ways that AI technologies can potentially reinvent businesses. But one thing is already clear: they must invest time and money to be prepared with sufficiently automated and structured data analytics in order to take full advantage of the new technologies. Like it or not, you cant afford to skip the basics.

More:

If Your Company Isn't Good at Analytics, It's Not Ready for AI - Harvard Business Review

The performance of humans puny brains will be outmatched by computers within just 13 years, billionaire Elon Musk has claimed.

TheTesla and SpaceX foundersaid that artificial intelligence will beat us at just about everything by 2030.

He made the comments on Twitter, where he was responding to a new study which claims our race will be overtaken by 2060.

Probably closer to 2030 to 2040 in my opinion, he wrote.

According to the terrifying research from boffs at the University of Oxford, its not looking good for us humans.

Machines will be better than us at translating languages by 2024 and writingschool essays by 2026, they claimed.

Within ten years computers will be better at driving a truck than us and by 2031 they will be better atselling goods and will put millions of retail workers on the dole queue.

AI will write a bestselling book by 2049 and conduct surgery by 2053, the researchers suggested.

In fact, every single human jobwill be automated within the next 120 years,according to computer experts the university researchers quizzed.

It's unlikely to trouble the billionaire tech entrepreneur, however.

Musk already has plans to plug our brains into computers.

He recently launched a new neuroscience company which aims to develop cranial computers that can download thoughts and possibly even treat disorders such as epilepsy and depression,the New York Post reported.

Over the years, the 45-year-old hasconjured up new ideas for space rocketsand electric-cars, proven that they can work efficiently, and then rolled them out for public and private use.

He's even hoping to start a human colony on Mars by 2030.

He's not alone in his estimations for the great computer takeover, either.

Scientists reckon humans are on the brink of a new evolutionary shift and man as we know it "probably won't survive".

In a terrifying advance, some have warned that computers are so advanced, those developing the complex formulas that make them "tick" aren't even sure howthey work.

And because they cannot understand the mechanical brains they have built, they fear that wecould lose control of them altogether.

That means they could behave unexpectedly - potentially putting lives at risk.

Take the case of driverless cars, for example where an algorithm might behave differently to normal and cause a crash.

Read more:

Elon Musk says artificial intelligence will beat humans at 'everything' by 2030 - Fox News

As research teams at Google, Microsoft, Facebook, IBM, and even Amazon have broken new ground in artificial intelligence in recent years, Apple always seemed to be the odd man out. It was too closed off to meaningfully integrate AI into the companys softwareit wasnt a part of the research community, and didnt have developer tools available for others to bring AI to its systems.

Thats changing. Through a slew of updates and announcements today at its annual developer conference, Apple made it clear that the machine learning found everywhere else in Silicon Valley is foundational to its software as well, and its giving developers the power to use AI in their own iOS apps as well.

The biggest news today for developers looking to build AI into their iOS apps was barely mentioned on stage. Its a new set of machine learning models and application protocol interfaces (APIs) built by Apple, called Core ML. Developers can use these tools to build image recognition into their photo apps, or have a chatbot understand what youre telling it with natural language processing. Apple has initially released four of these models for image recognition, as well as an API for both computer vision and natural language processing. These tools run locally on the users device, meaning data stays private and never needs to process on the cloud. This idea isnt neweven data hoarders like Google have realized the value of letting users keep and process data on their own devices.

Apple also made it easy for AI developers to bring their own flavors of AI to Apple devices. Certain kinds of deep neural networks can be converted directly into Core ML. Apple now supports Caffe, an open-source software developed by the University of California-Berkeley for building and training neural networks, and Keras, a tool to make that process easier. It notably doesnt support TensorFlow, Googles open-source AI framework, which is by far the largest in the AI community. However, theres a loophole so creators can build their own converters. (I personally expect a TensorFlor converter in a matter of days, not weeks.)

Some of the pre-trained machine learning models that Apple offers are open-sourced Google code, primarily for image recognition.

Apple made it clear in the keynote today that every action taken on the phone is logged and analyzed by a symphony of machine-learning algorithms in the operating system, whether its predicting when you want to make a calendar appointment, call a friend, or make a better Live Photo.

The switch to machine learning can be seen in the voice of Siri. Rather than using the standard, pre-recorded answers that Apple has always relied on, Siris voice is now entirely generated by AI. It allows for more flexibility (four different kinds of inflection were demonstrated on stage), and, as the technology advances, it will sound exactly like a human anyway. (Apples competitors are not far off.)

Apple also rattled off a number of other little tweaks powered by ML, like the iPad distinguishing your palm from the tip of an Apple Pencil, or dynamically extending the battery life of the device by understanding which apps need to consume power.

Okay, so Apples really only published one paper. But it was a good one! And Ruslan Salakhutdinov, Apples new director of AI research, has been on the speaking circuit. He recently spoke at Nvidias GPU Technology Conference (although Apples latest computers use AMD chips), and will be speaking later this month in New York City, to name a few.

Apple also held a closed-door meeting with their competitors at a major AI conference late last year, shortly after Salakhutdinov was hired, to explain what it was working on in its labs. Quartz obtained some of those slides and published them here.

Is Apple a leader in AI research? Not according to most metrics. But many consider open research to be a way of recruiting top talent in AI, so we might see more papers and talks in the future.

Read the original:

Apple is finally serious about artificial intelligence - Quartz

Given the speed at which researchers are advancing artificial intelligence, the question has become not if A.I. will become smarter than its human creators, but when?

A team of researchers from Yale University and Oxford's Future of Humanity Institute recently set off to determine the answer. During May and June of 2016, they polled hundreds of industry leaders and academics to get their predictions for when A.I. will hit certain milestones.

The findings, which the team published in a study last week: A.I. will be capable of performing any task as well or better than humans--otherwise known as high-level machine intelligence--by 2060 and will overtake all human jobs by 2136. Those results are based on the 352 experts who responded.

Monday night, Elon Musk, who's been a consistent A.I. fear monger, chimed in on Twitter.

The entrepreneur followed up his tweet with an ominous, "I hope I'm wrong." Musk has been a vocal critic of A.I. the past several years, painting nightmare scenarios in which it becomes weaponized or outsmarts humans and leads to their extinction. He co-founded OpenAI, a non-profit that aims to ensure A.I. is used for good, in 2015.

Musk's own firm, Tesla, is one of the companies leading the charge in creating self-driving vehicles. The trucking and taxi industries employ about 2 million Americans, all of whom could soon find their jobs obsolete should vehicles become fully autonomous.

The experts polled in the study predicted that A.I. would become better at driving trucks than humans in 2027. The surveys were completed before robotics startup Otto successfully sent a self-driving truck on a 120-mile journey in October.

A.I. will surpass humans in a number of other milestones, the experts suggested: translating languages (2024), writing high-school level essays (2026), and performing surgeries (2053). They estimated that it would be able to write a New York Times bestseller in 2049.

In May, Google's AlphaGo machine won a game of Go against China's Ke Jie, widely considered to be the world's best player. An A.I. system created by scientists at Carnegie Mellon won $2 million from top poker players in a tournament in January.

It's worth noting that the predicted timelines did not vary based on the experts' levels of experience with artificial intelligence. One variable that did correlate with the predictions was the location: North American experts thought A.I. would outperform humans on all tasks within 74 years, while experts in Asia thought this would take only 30 years. The researchers who published the study didn't provide a potential explanation for the discrepancy.

More:

Elon Musk (and 350 Experts) Predict Exactly When Artificial ... - Inc. - Inc.com

One of the ways companies fill their ranks with good employees is by scouting passive talent, or people who arent currently looking for new jobs but might be convinced with the right offer. This usually takes hours of networking, but a Tel Aviv-headquartered startup called Workey uses artificial intelligence to streamline the process by matching companies with potential candidates. Workey launched in the U.S. today and also announced that it has raised $8 million in Series A funding.

The round was led by PICO Partners and Magma VC and brings the total Workey has raised so far to $9.6 million, including its earlier seed funding. Workey will use the new capital to expand in the U.S., open an office in New York City, and hire people for its research and development and data science teams.

A LinkedIn study released last year found that recent college graduates are more likely to switch jobs at least twice before their early 30s than previous generations. Workey targets people who are interested in potential opportunities, but dont want to broadcast their curiosity to everyone, including their current employers. Once they sign up for the site, they create an anonymous profile that is used to find positions their background and skills qualify them for.

Workeys recommendation system then matches companies with promising candidates. If a company requests an introduction through the site, users can respond by revealing their full details. Otherwise, all rejections are anonymous. As an example, Workeys co-founders say Yahoo has found several candidates by spending 10 minutes a week on Workey.

Founded in 2015 by Ben Reuveni, Danny Shteinberg, and Amichai Schreiber, Workey has worked with more than 400 companies so far, including Yahoo, Amazon, Dell EMC, and Oracle. In a group interview by email, the trio told TechCrunch that the anonymous platform helps mitigates hiring bias, because companies dont see a candidates race, gender, ethnicity, or religion first. It also allows candidates to see how they stand in relation to the rest of the job market, which can help them during wage negotiations.

Another benefit is combatting the stigma associated with job seekers.

Like it or not, there is much truth to the belief that candidates who are currently working are more desirable than those who are out of a job and full-time job hunting, Workeys founders explained. Passive talent, those who are not actively looking but wouldnt want to miss out on their dream job, are often the most desirable candidates since they typically are already secure in their current position (likely because they perform them well).

Once they do decide to interview for a new job, Workey lets candidates track the status of their application, so they dont spend weeks in limbo waiting for an offer or rejection. The startup works mainly with tech companies right now, because it was invented by engineers for engineers, but can be adapted for other industries. Its free for job candidates and monetizes by charging companies a fee, but its founders claim that they potentially save thousands of dollars by using Workeys AI instead of headhunters or recruitment agencies.

Workey isnt the only career services startup that wants to use AI to streamline the recruitment process, which often takes months. Other companies that have developed AI tools to improve or replace headhunting, job searches, or interviews include Engage, FirstJob, Arya, and Mya. Though their services dont necessarily overlap with Workey right now, its a sign that Workeys competition is likely to increase soon. But its founders insist that one of the most exciting aspects of business today is that there is no future-proofing. Workey will continue to evolve and grow, with a continued investment in R&D to ensure that we provide users with the best possible matches enhancing their careers.

Read more:

Career site Workey raises $8M to replace headhunters with artificial ... - TechCrunch