The two companies have partnered to develop an AI-enabled platform, "One", to create a range of seasoning blends.

AI chefs may seem like they belong to a far-off future, however, IBM and McCormick may have taken us one step closer after the two companies announced a partnership to use AI to create new flavors in food product development.

The AI-enabled platform, named "One", uses the technology to learn and predict new flavor combinations using "hundreds of millions" of data points from sensory science, consumer preference and flavor palettes. It combines IBM's expertise in AI with McCormick's knowledge of consumer taste, including product formulas from the past few decades.

The new range of seasoning blends for proteins and vegetables are expected to be in shops across the US by late-1Q19.

McCormick CEO Lawrence Kurzius remarked: "McCormick's use of AI highlights our commitment to insight-driven innovation and the application of the most forward-looking technologies to continually enhance our products and bring new flavors to market.

"This is one of several projects in our pipeline where we've embraced new and emerging technologies."

IBM vice president of industry research Kathryn Guarini added: "IBM Research's collaboration with McCormick illustrates our commitment to helping our clients and partners drive innovation across industries.

"By combining McCormick's deep data and expertise in science and taste, with IBM's AI capabilities, we are working together to unlock the bounds of creativity and transform the food and flavor development process."

McCormick plans to share the data with its peers globally in the future, with the intent to upscale the technology by 2021.

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As reporters and editors find themselves the victims of layoffs at digital publishers and traditional newspaper chains alike, journalism generated by machine is on the rise.

Roughly a third of the content published by Bloomberg News uses some form of automated technology. The system used by the company, Cyborg, is able to assist reporters in churning out thousands of articles on company earnings reports each quarter.

The program can dissect a financial report the moment it appears and spit out an immediate news story that includes the most pertinent facts and figures. And unlike business reporters, who find working on that kind of thing a snooze, it does so without complaint.

Untiring and accurate, Cyborg helps Bloomberg in its race against Reuters, its main rival in the field of quick-twitch business financial journalism, as well as giving it a fighting chance against a more recent player in the information race, hedge funds, which use artificial intelligence to serve their clients fresh facts.

“The financial markets are ahead of others in this,” said John Micklethwait, the editor in chief of Bloomberg.

In addition to covering company earnings for Bloomberg, robot reporters have been prolific producers of articles on minor league baseball for The Associated Press, high school football for The Washington Post and earthquakes for The Los Angeles Times.

Last week, The Guardian’s Australia edition published its first machine-assisted article, an account of annual political donations to the country’s political parties. And Forbes recently announced that it was testing a tool called Bertie to provide reporters with rough drafts and story templates.

As the use of artificial intelligence has become a part of the industry’s toolbox, journalism executives say it is not a threat to human employees. Rather, the idea is to allow journalists to spend more time on substantive work.

“The work of journalism is creative, it’s about curiosity, it’s about storytelling, it’s about digging and holding governments accountable, it’s critical thinking, it’s judgment — and that is where we want our journalists spending their energy,” said Lisa Gibbs, the director of news partnerships for The A.P.

The A.P. was an early adopter when it struck a deal in 2014 with Automated Insights, a technology company specializing in language generation software that produces billions of machine-generated stories a year.

In addition to leaning on the software to generate minor league and college game stories, The A.P., like Bloomberg, has used it to beef up its coverage of company earnings reports. Since joining forces with Automated Insights, The A.P. has gone from producing 300 articles on earnings reports per quarter to 3,700.

The Post has an in-house robot reporter called Heliograf, which demonstrated its usefulness with its coverage of the 2016 Summer Olympic Games and the 2016 elections. Last year, thanks to Heliograf, The Post won in the category of Excellence in Use of Bots at the annual Global Biggies Awards, which recognize accomplishments in the use of big data and artificial intelligence. (As if to make journalists jittery, the Biggies ceremony took place at Columbia University’s Pulitzer Hall.)

Jeremy Gilbert, the director of strategic initiatives at The Post, said the company also used A.I. to promote articles with a local orientation in topics like political races to readers in specific regions — a practice known as geo-targeting.

“When you start to talk about mass media, with national or international reach, you run the risk of losing the interest of readers who are interested in stories on their smaller communities,” Mr. Gilbert said. “So we asked, ‘How can we scale our expertise?’”

The A.P., The Post and Bloomberg have also set up internal alerts to signal anomalous bits of data. Reporters who see the alert can then determine if there is a bigger story to be written by a human being. During the Olympics, for instance, The Post set up alerts on Slack, the workplace messaging system, to inform editors if a result was 10 percent above or below an Olympic world record.

A.I. journalism is not as simple as a shiny robot banging out copy. A lot of work goes into the front end, with editors and writers meticulously crafting several versions of a story, complete with text for different outcomes. Once the data is in — for a weather event, a baseball game or an earnings report — the system can create an article.

But machine-generated stories are not infallible. For an earnings report article, for instance, software systems may meet their match in companies that cleverly choose figures in an effort to garner a more favorable portrayal than the numbers warrant. At Bloomberg, reporters and editors try to prepare Cyborg so that it will not be spun by such tactics.

A.I. in newsrooms may also go beyond the production of rote articles.

“I hope we’ll see A.I. tools become a productivity tool in the practice of reporting and finding clues,” said Hilary Mason, the general manager for machine learning at Cloudera, a data management software company. “When you do data analysis, you can see anomalies and patterns using A.I. And a human journalist is the right person to understand and figure out.”

The Wall Street Journal and Dow Jones are experimenting with the technology to help with various tasks, including the transcription of interviews or helping journalists identify “deep fakes,” the convincingly fabricated images generated through A.I.

“Maybe a few years ago A.I. was this new shiny technology used by high tech companies, but now it’s actually becoming a necessity,” said Francesco Marconi, the head of research and development at The Journal. “I think a lot of the tools in journalism will soon by powered by artificial intelligence.”

The New York Times said it had no plans for machine-generated news articles, but the company has experimented with using A.I. to personalize newsletters, help with comment moderation and identify images as it digitizes its archive.

Previous technological advances have rendered moot a number of jobs that were once essential to the journalism industry, such as Linotype operator. But reporters and editors have not yet been tempted to smash the programs now taking care of some of the busy work that once fell to them.

“When you look at the ways things are laid out and printed and produced and distributed, a lot of those functions have been replaced with technology,” said Nastaran Mohit, the organizing director for the News Guild of New York. She added that she did not consider A.I. a threat to newsroom workers, while also noting that the guild monitors emerging technologies to make sure that hypothesis holds true.

Mr. Marconi of The Journal agreed, likening the addition of A.I. in newsrooms to the introduction of the telephone. “It gives you more access, and you get more information quicker,” he said. “It’s a new field, but technology changes. Today it’s A.I., tomorrow it’s blockchain, and in 10 years it will be something else. What does not change is the journalistic standard.”

Marc Zionts, the chief executive of Automated Insights, said that machines were a long way from being able to replace flesh-and-blood reporters and editors. He added that his daughter was a journalist in South Dakota — and although he had not advised her to leave her job, he had told her to get acquainted with the latest technology.

“If you are a non-learning, non-adaptive person — I don’t care what business you’re in — you will have a challenging career,” Mr. Zionts said.

For Patch, a nationwide news organization devoted to local news, A.I. provides an assist to its 110 staff reporters and numerous freelancers who cover about 800 communities, especially in their coverage of the weather. In a given week, more than 3,000 posts on Patch — 5 to 10 percent of its output — are machine-generated, said the company’s chief executive, Warren St. John.

In addition to giving reporters more time to pursue their interests, machine journalism comes with an added benefit for editors.

“One thing I’ve noticed,” Mr. St. John said, “is that our A.I.-written articles have zero typos.”

Correction: 

An earlier version of this article misstated the name of a tool that Forbes is testing for reporters. It is Bertie, not Birdie.

Follow Jaclyn Peiser on Twitter: @jackiepeiser.

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Finding the right chemotherapy dosage is a grueling process of trial and error, leading Stanford University Hospital researchers to train AI to identify correct dosage before treatment begins

A group of researchers from Stanford University Hospital have successfully used AI technology to accurately adjust chemotherapy dosages by identifying those who required a lower dose even before treatment had begun, according to a study published in Nature Scientific Reports.

Finding the right dose of medication for chemotherapy treatment uses a process of trial and error which can cause unnecessary suffering for patients. Adverse effects from taking the wrong dose of medication results in an estimated 280,000 hospitalizations in the US every year, according to the report.

To discover the correct dosage, the team used the "Random Forest Classifier" method, an algorithm which combines the results of decision trees based on slightly different subsets of data. To train the algorithm, the researchers fed it digital information on patients who needed to have their dosages changed from various sources, including analysis results, notes and prescriptions.

"The result of their research illustrates the role that machine learning can play in the initial dosage of drugs associated with a variable response and shows how the health system can gain from being computerized," read theNature Scientific Reports report.

Each year, about 650,000 cancer patients receive chemotherapy treatment in the US, according to the Centers for Disease Control and Prevention. Using AI technology may dramatically improve outcomes and quality of life for patients undergoing treatment.

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The driver in a car accident takes a picture of the damaged vehicle and sends it to an insurerfor a coverage quote on the spot. A hat retailer uses data analytics to tweak its marketing formula and more than 60 percent of recipients suddenly open their messages in an email campaign. A hotel guest checks in and issues voice commands to an in-room personal assistant, ordering a rental car from the guest’s preferred company that shows up outside the lobby a half-hour later.

Is this the future of artificial intelligence, or is it a mad vision of computers run amok? In fact, these are all actual use cases presented during Dreamforce 2018 in San Francisco this week (pictured), and they underscore a theme that occupied much of the conversation among 170,000 attendees. Conference organizer Salesforce has been working hard on artificial intelligence since it rolled out Einstein two years ago and it may be making significant progress in a challenging and often overhyped field.

The stakes for Salesforce and every other company seeking to build a viable business in the AI space are high. It takes a significant capital investment to do it right, yet customers demand it.

Doubling down on Einstein

“What’s coming next is AI,” Ulrich Spiesshofer, president and chief executive of global industrial solutions giant ABB Ltd., said during a conversation on Wednesday with Salesforce co-CEO Marc Benioff on a Dreamforce stage. “We need to be leading in AI use as an industry.”

ABB, which announced a significant in-house expansion of Salesforce’s Einstein AI technology this week, has built its business largely on the capabilities of intelligent industrial robots. The company created its own viral marketing stir last year when it had one of its robots conduct an orchestra in Pisa, Italy, while accompanied by the famed tenor Andrea Bocelli.

“We’re using AI combined with unique hardware to create a completely unique market,” Spiesshofer told Benioff.

AI use cases extend far beyond the entertainment value of robots conducting a Verdi opera before a rapturous Italian crowd. Auction Nation, a South African car auction company, depends on AI to sell salvaged vehicles sourced from top insurers. Using pictures taken of the damaged cars, Auction Nation employs Einstein Vision and Einstein Discovery to build an instant 3-D model and compare the result with previously sold cars for analysis of who might buy them.

“This model isn’t perfect yet,” said Errol Levin, chief operating officer at Auction Nation, during a Dreamforce presentation on Wednesday morning. “But it’s like a baby’s brain. The model is becoming more and more intelligent.”

Glimpse of AI future

Shortly before Dreamforce two years ago, Salesforce acquired MetaMind, a Silicon Valley-based AI startup. MetaMind’s CEO was Richard Socher, who as a Stanford doctoral student had conducted extensive research in deep learning, computer vision and natural language processing or NLP.

Socher is currently chief scientist at Salesforce and on Wednesday afternoon he offered conference attendees a peek behind the curtain at what the future of AI may hold as his company continues to pursue a number of research initiatives.

In the area of computer vision, Socher displayed a set of pictures including one with a little girl sitting on a bench and holding an umbrella. The computer could successfully identify girl, bench and umbrella when asked. In another example, the stripes on a cat’s tail were correctly called out. “We’re seeing more of these visual capabilities actually making it into production,” Socher said.

But the primary focus of the Salesforce research team is on NLP, not surprising given Socher’s previous academic work. He has published a lengthy body of research in the field.

One member of Socher’s research team is Bryan McCann, who joined Salesforce around the time of the MetaMind acquisition. McCann also studied at Stanford and was a course assistant for Andrew Ng, a specialist in AI and the previous founder of Google Brain.

Making strides in language

On Wednesday, McCann presented the results of NLP work which revealed significant progress in the ability of computers to understand language context. The focus of the Salesforce team’s research was on successfully completing the “decaNLP,” a new benchmark that requires successfully performing 10 disparate natural language programming tasks.

According to McCann, the NLP solution developed by the Salesforce team can now do all 10 “fairly well” by creating a multitask language model. “We took 10 of the hardest tasks we could find in NLP,” McCann said. “And we used the natural language decathlon to guide our decisions.”

The results were intriguing. In a live onstage research demonstration, McCann showed the audience how the Salesforce model could ingest the convoluted text of a lengthy corporate press release and neatly summarize the content in fewer than 20 words. In another example, the model was told that no one in the audience had clapped during McCann’s presentation and was asked whether he was sad or happy. “Sad” came back as the computerized response.

“This model is able to do a lot of different things,” McCann said. “It’s how we can unify anything in NLP.”

How all of this will translate into business success for Salesforce remains to be seen, but the company shows no sign of taking its foot off the pedal of the AI car. A significant focus by the company at Dreamforce this week has been on its Einstein technology and Salesforce acquired yet another AI provider – Datorama – two months ago.

“We see a future that is conversational, contextual and intuitive,” Socher said. It’s also competitive. Salesforce delivered a clear message that it intends to be a major player.

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If we produce something capable of passing the Turing test – something capable of mimicking human responses under certain conditions to such a degree that it can be declared true artificial intelligence – what does that mean? What is sentience? Is it empathy? Is empathy created or innate? If we create an artificial intelligence capable of displaying empathy, does that mean empathy is engineered, or have we managed to create true intelligence in a computer brain? 

The very mention of AI evokes these kinds of philosophical debates. People treat AI like they treat Jurassic Park – they view the concept of artificial intelligence as one of humanity trying to play the role of God and being doomed to extinction rooted in hubris once the creation surpasses the creator.

The reality, of course, is that we are using AI all the time now, at least in some form or another. AI has revolutionized almost every industry and will continue to do so. In this article, we will examine how AI has changed the app development, travel, debt, retail, and IT industries so that we may better understand how AI interacts with business.

How AI Affects App Development

Check the Instagram explore page under the or you’ tab. Chances are, you’ll find a number of accounts to follow that are interesting and surprisingly tailored towards your interests. This is one of the simplest examples of AI – predictive reasoning.

Instagram’s AI is analyzing what accounts you follow, what accounts you visit, and what posts you like, every time you use the app. It then takes that data to create suggestions of who you should follow on the explore page. This kind of AI within apps has become commonplace today, with everything from Amazon Go to the Starbucks app integrating similar predictive reasoning based suggestive AI features.

This is just a small example of how AI has drastically impacted app development – no longer do app developers need to create complex codes to create generic explore pages. Now, an AI can constantly update and create new user experiences specifically tailored to each user of the app – this makes AI quite possibly the most powerful marketing tool that apps can use in today’s business environment. But it’s not just app development that is affected by AI.

How AI Affects the Travel Industry 

You know how every time you book a flight, the nearest hotels and restaurants are recommended to you, ordered by price and location? That’s an AI trying to help out. Much in the same way apps use AI, the travel and tourism industry thrives on AI and localization to make travel experiences better.

From camping to five star hotels, travel websites and supply stores will often use AI to help you plan your trip. In fact, there are downloadable travel AI concierges, called chatbots, that will directly help you plan your trip, whether it’s in buying the best tent for a given price range, or in arranging the best suite to stay in at the Cosmopolitan.

AI has revolutionized the travel industry, scouring the internet to consistently give you the best and most educated decisions to make your travel experiences as painless as possible. In fact, AI can also impact services such as debt collection.

How AI Affects Debt Collection

People almost always cite debt collection as a consistent stressor in their lives. AI automates the debt collection process, sending emails, texts, and pre-recorded calls to customers. Although this sounds like the norm, it results in customers feeling much less harassed by debt collection agencies. No longer do they receive five phone calls throughout their work day, instead they simply receive one call and one email, tailored to match their schedule by AI.

AI in the debt collection agency is a fantastic example of how AI can actually make life easier for the people affected by it. The success of AI in this industry alone, and the improvements it has made for customers in this industry, should remove some of the stigma surrounding AI.

How AI Affects Retail

Have you ever shopped on a site and bought a pair of camo pants, only to see a blurb pop up recommending other military clothing products via a message like customers who bought these pants also loved these shirts!’ If you have, that’s AI at work once again. That’s what Aussie Disposals implemented on their store, and they love what their AI developers did.

Although Amazon is the easiest example of this phenomenon, most online retail stores now feature an AI that tracks and evaluates your shopping habits to recommend new items to you. This is just another example of how AI can improve customer service.

How AI Affects IT Service Management and Software Development

From customer support to software development, AI has its uses. To begin, AI is an effective way of troubleshooting basic problems, making it a useful customer support tool in IT service management.

Serving in a more complicated niche, AI can also help software engineers develop new products. From running tests faster than an engineer ever could, to predictive reasoning-based problem solving, AI is rapidly becoming a primary tool in software development. AI is also extremely useful in analyzing data stored in cloud storage tools and using that data to create advantages for everyone from software engineers to business owners.

And speaking of AI in software…

AI in AR and VR

AR and VR have made leaps and bounds, and AI can only help. AI has the potential to take AR and VR apps to the next level by tailoring these experiences to specific users according to their habits. This ability only further cements AI as a staple of the app development industry in the modern world.

In Conclusion…

It is not a stretch to say that AI has found a use in every industry. Whether it be for simple tasks such as suggesting products or providing customers with basic customer service, or for complicated measures such as running software tests and completing extensive problem-solving procedures for industries such as debt collection, AI has found its place in our world, and it’s safe to say that it’s here to stay.

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