I recently received an email from the folks over at Medium, an online publication I subscribe to. Here’s what it said:

“Hello! It looks like you’re into artificial intelligence, so we rounded up a few of our favorites for you this week. Because you’re a member and an avid reader, we want to make sure you are getting the best stories in the topics that interest you.”

The email went on to give me a list of articles I might be interested in. And they were right, I was interested.

CREEPED OUT

But here’s the thing: that email was certainly not sent by “folks” — at least not in the way we used to think about them. That was a message about AI — sent by AI! An algorithm was studying my reading patterns, decided I was interested in articles about algorithms like itself and sent me a mail recommending that I read more about its brethren…

Are you enthused or creeped out? Reactions vary …

Meanwhile, over at Google, CEO Sundar Pichai was showcasing their own breakthroughs in AI. Specifically, their Google Duplex virtual assistant, which can impersonate human beings to make calls on our behalf. If you haven’t seen the demo, stroll over to YouTube and do so.

ANOTHER HUMAN

The AI assistant calls a salon to make an appointment. In the demo, it is able to talk exactly like a human (complete with pauses and verbal ticks) — and think on its feet when the desired time is not available. In a second instance, the voice bot calls to make a restaurant booking. In this case, the human being on the other side does not understand the question well — but the bot steers the conversation remarkably effectively towards a satisfactory conclusion. More effectively than the human being.

According to Google, those were actual calls made by bots. And it was pretty much impossible for the humans on the other side of the call to realise they were not talking to another human.

Wow. Even if Google might be gaming this a little and only showing us the best recorded calls, I trust you understand the implications: we are not that far from a situation when bots will be engaging with us on a regular basis, and none of us will be able to tell the difference. What happens in such a world?

DARK SIDE

There is, of course, the displacement of human beings to think about. If bots are already this good at talking and deciding, what happens to the armies of humans we have been employing to do these tasks?

Remember AI bots can be designed to learn as they go, and get better and better, with little human supervision eventually needed.

And is it just low-level jobs at risk? Not so long ago a computer science professor at Georgia Tech designed an artificial teaching assistant called “Jill Watson” to answer students’ questions online.

The students could not tell the difference between Jill and human assistants, and were only brought into the secret at the end of the semester.

There’s another dark side to this revolution. The world is already infested with social-media bots that spread fake news and influence entire elections by swaying gullible voters. They can create not just fake news and images, but fake video as well.

EXPERIMENT NOW

And so great caution is needed. If flawless human impersonation is possible, then will we all soon be taken in by scams where bots sound like they are our trusted friends or organisations.

The human race needs to be ready to embrace this remarkable technology — but also to regulate and harness it. We need a plan for jobs, and a plan to prevent abuses before they spiral out of control.

For those running businesses and institutions, I trust a specific thought has occurred to you upon reading this column: AI is no longer a bit of science fiction you can afford to ignore; it’s right here, right now. It will transform the economics of business, and change the way you think about a “workforce”.

Work on earth will soon be done by a mixture of humans, humanoids and lines of code. You need to be thinking hard about the opportunities and the dangers. Don’t wait until it’s over and others have won the race. Get thinking and experimenting now.

And what if this very column warning you about AI was written by AI? That too is coming.

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Earlier this month, Google’s Chief Executive Officer Sundar Pichai unveiled the new and improved Google News. According to Pichai, the app could now show “the news you care about from trusted sources while still giving you a full range of perspectives on events.”

The company is reportedly working on integrating artificial intelligence in its news app in an effort to eliminate disinformation and help people get more viewpoints that are beyond their so-called “filter bubble.”

The new initiative was said to be part of Google’s goal to be at the center of online news. This also includes new efforts to help advertisers and publishers gain paid subscribers using the tech company’s platform.

Google’s product chief Trystan Upstill also added that the Google News would make use of the “best artificial intelligence to find the best of human intelligence,” which, according to Google, is the reporting done by local and international journalists.

Aside from letting users customize their news feeds, the new app will also include the top stories for all kinds of readers. The app was reportedly designed this way to break the bubble of information that’s being filtered to fuel peoples’ biases.

“Having a productive conversation or debate requires everyone to have access to the same information,” Upstill was quoted as saying.

According to Upstill, their “full coverage” feed would be for everyone,” as an unpersonalized view of events from a range of trusted news sources.

While Google’s advancement in AI is said to be astounding, many editors and journalists are not in favor of the tech giant’s plans to apparently replace human editors with machines.

“There’s been a fantasy of (algorithmic) personalized news for a long time,” Meredith Broussard, New York University journalism professor, said. “Nobody has ever gotten it right. I think that news designers and homepage editors do a good job of curating already.”

However, there are still some experts, like Dan Kennedy of the Northeastern University, who believe that Google’s effort to integrate AI in its news app is a positive approach that will benefit the current news ecosystem.

“For many years, Google refused to share ad revenues with news organizations on the grounds that Google was driving traffic to them, and that it was up to those organizations to figure out how to monetize that traffic,” Kennedy was quoted as saying.

“NOW, WITH MORE THAN 90 PERCENT OF ALL NEW DIGITAL AD REVENUES GOING TO GOOGLE AND FACEBOOK, GOOGLE IS FINALLY ACKNOWLEDGING THAT IT’S TIME TO TRY SOMETHING ELSE.”

Aside from Google, Facebook has also faced the wrath of people for reportedly being an instrument in spreading fake news. This caused the company to make significant changes in its news feeds algorithm.

The post Google News to get Artificial Intelligence Upgrade appeared first on Artificial Intelligence.

WASHINGTON: For its updated news application, Google is doubling down on the use of artificial intelligence as part of an effort to weed our disinformation and help users get viewpoints beyond their own “filter bubble.”

Google chief Sundar Pichai, who unveiled the updated Google News earlier this month, said the app now “surfaces the news you care about from trusted sources while still giving you a full range of perspectives on events.”

It marks Google’s latest effort to be at the center of online news and includes a new push to help publishers get paid subscribers through the tech giant’s platform.

According to product chief Trystan Upstill, the news app “uses the best of artificial intelligence to find the best of human intelligence — the great reporting done by journalists around the globe.”

While the app will enable users to get “personalized” news, it will also include top stories for all readers, aiming to break the so-called filter bubble of information designed to reinforce people’s biases.

“Having a productive conversation or debate requires everyone to have access to the same information,” Upstill said.

He said the “full coverage” feed would be the same for everyone — “an unpersonalized view of events from a range of trusted news sources.”

Some journalism industry veterans were skeptical about the effort to replace human editors with machine curators.

“There’s been a fantasy of (algorithmic) personalized news for a log time,” said New York University journalism professor Meredith Broussard.

“Nobody has ever gotten it right. I think that news designers and home page editors do a good job of curating already.”

Google and Facebook have also been criticized for scooping up most online ad revenues and for enabling false information to spread.

Recently, News Corp. CEO Robert Thomson called for an “algorithm review board” that would “oversee these historically influential digital platforms and ensure that there is no algorithmic abuse or censorship.”
Changing news ecosystem 
In the new app, Google’s “newsstand” addresses some concerns by allowing users to sign up for subscriptions using their Google accounts, and will enable publishers to connect with readers directly.

Dan Kennedy, a Northeastern University journalism professor, said the revamped news app appears to be positive for the news ecosystem.

“For many years, Google refused to share ad revenues with news organizations on the grounds that Google was driving traffic to them, and that it was up to those organizations to figure out how to monetize that traffic,” Kennedy said.

“Now, with more than 90 percent of all new digital ad revenues going to Google and Facebook, Google is finally acknowledging that it’s time to try something else.”

Nicholas Diakopoulos, a Northwestern University professor specializing in computational and data journalism, said the impact of Google’s changes remain to be seen.

Diakopoulos said algorithmic and personalized news can be positive for “engagement” but may only benefit a handful of news organizations.

His research found that “Google concentrates its attention on a relatively small number of publishers, it’s quite concentrated.”

Google’s effort to identify and prioritize “trusted” news sources may also be problematic, according to Diakopoulos.

“Maybe it’s good for the big guys, or the (publishers) who have figured out how to game the algorithm,” he said. “But what about the local news sites, what about the new news sites that don’t have a long track record?”

Diakopoulos said that while AI can offer some advantages in news curation, “you still need people involved in many different ways. you need to reflect human values, editorial values… you can’t quantify every aspect that might be important to an editorial decision.”

The growing importance of the platforms raise the questions of whether Google and Facebook are “media” companies and not simply technology firms, a moniker both have resisted.

Diakopoulos said Google “is becoming more and more like a media company,” although that is sometimes difficult to define.

“Yahoo started as a tech company and became a media company, and maybe Google is headed in that direction,” he said.

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Artificial intelligence (AI) and machine learning are transforming the global economy, and companies that are quick to adopt these technologies will take $1.2 trillion from those who don’t. Businesses that fail to take advantage of predictive analytics, or don’t have the time or resources – like highly-trained (and expensive) data scientists – will fall behind organizations that embrace AI and machine learning to extract business value from their data.

Enter automated machine learning, a new class of solutions for accelerating and optimizing the predictive analytics process. Incorporating the experience and expertise of top data scientists, automated machine learning automates many of the complex and repetitive tasks required in traditional data science, while providing guardrails to ensure critical steps are not missed. The bottom line: data scientists are more productive and business analysts and other domain experts are transformed into “citizen data scientists” that have the ability to create AI solutions.

As more so-called “automated machine learning” tools are brought to market, often with limited feature sets, there is a need to define the requirements for a true automated machine learning platform. This article highlights the 10 capabilities that must be addressed to be considered a complete automated machine learning solution.

1. Preprocessing of Data

Each machine learning algorithm works differently, and has different data requirements. For example, some algorithms need numeric features to be normalized, and some require text processing that splits the text into words and phrases, which can be very complicated for languages like Japanese. Users should expect their automated machine learning platform to know how to best prepare data for every algorithm and following best practices for data partitioning.

2. Feature Engineering

Feature engineering is the process of altering the data to help machine learning algorithms work better, which is often time-consuming and can be expensive. While some feature engineering requires domain knowledge of the data and business rules, most feature engineering is generic. A true automated machine learning platform will engineer new features from existing numeric, categorical, and text features. The system should understand which algorithms benefit from extra feature engineering and which don’t, and only generate features that make sense given the data characteristics.

3. Diverse Algorithms

Every dataset contains unique information that reflects the individual events and characteristics of a business. Due to the variety of situations and conditions represented in the data, one algorithm cannot successfully solve every possible business problem or dataset. Automated machine learning platforms need access to a diverse repository of algorithms to test against the data in order to find the right algorithm to solve the challenge at hand. And, the platform should be updated continually with the most promising new machine learning algorithms, including those from the open source community.

4. Algorithm Selection

Having access to hundreds of algorithms is great, but many organizations don’t have the time to try every algorithm on their data. And some algorithms aren’t suited to their data or data sizes, while others are extremely unlikely to work well on their data altogether. An automated machine learning platform should know which algorithms are right for a business’ data and test the data on only the appropriate algorithms to achieve results faster.

5. Training and Tuning

It’s standard for machine learning software to train an algorithm on the data, but often there is still some hyperparameter tuning required to optimize the algorithm’s performance. In addition, it’s important to understand which features to leave in or out, and which feature selections work best for different models. An effective automated machine learning platform employs smart hyperparameter tuning for each individual model, as well as automatic feature selection, to improve both the speed and accuracy of a model.

6. Ensembling

Teams of algorithms are called “ensembles” or “blenders,” with each algorithm’s strengths balancing out the weaknesses of another. Ensemble models typically outperform individual algorithms because of their diversity. An automated machine learning platform should find the optimal algorithms to blend, include a diverse range of algorithms, and tune the weighting of the algorithms within each blender.

7. Head-to-Head Model Competitions

It’s difficult to know ahead of time which algorithm will perform best in a particular modeling challenge, so it’s necessary to compare the accuracy and speed of different algorithms on the data, regardless of the programming language or machine learning library the algorithms come from. A true automated machine learning platform must build and train dozens of algorithms, comparing the accuracy, speed, and individual predictions of each algorithm and then ranking the algorithms based on the needs of the business.

8. Human-Friendly Insights

Machine learning and AI have made massive strides in predictive power, but often at the price of complexity and interpretability. It’s not enough for a model to score well on accuracy and speed – users must trust the answers. And in some industries, and even some geographies (see the EU’s  GDPR), models must comply with regulations and be validated by a compliance team. Automated machine learning should describe model performance in a human-interpretable manner and provide easy-to-understand reasons for individual predictions to help an organization achieve compliance.

9. Easy Deployment

An analytics team can build an impressive predictive model, but it is of little use if the model is too complex for the IT team to reproduce, or if the business lacks the infrastructure to deploy the model to production. Easy, flexible deployment options are a hallmark of a workable automated machine learning solution, including APIs, exportable scoring code, and on-demand predictions that don’t require the intervention of the IT team.

10. Model Monitoring and Management

Even the best models can go “stale” over time as conditions change or new sources of data become available. An ideal automated machine learning solution makes it easy to run a new model competition on the latest data, helping to determine if that model is still the best, or if there is a need to update the model. And as models change, the system should also be able to quickly update the documentation on the model to comply with regulatory requirements.

Businesses that turn to automated machine learning encompassing these features will save time, increase accuracy, and reduce compliance risk when building out their machine learning models – helping them become a truly AI-driven enterprise.

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