Category Archives: Ai

Nvidia has a new generation of its Jetson embedded computing platform for devices at the edge of a network, including things like traffic cameras, manufacturing robotics, smart sensors and more. The Jetson TX2 has twice the performance of its predecessor, the TX1, or it can also redirect efficiency to power savings, using less than half the power consumption of the original to achieve the same processing abilities.

The TX2 uses a Pascal-based GPU, as well as two 64-bit Nvidia quad-core ARM chips, with 8GB of RAM on board and 32GB of fast flash storage. It also features built-in 802.11ac Wi-Fi networking, Bluetooth connectivity and 1GB Ethernet for wired connections. It makes it possible to push edge-of-network computing even further, allowing for the running of distributed neural networks right on edge devices that can more accurately do things like identify objects in images, recognize speech or interpret surroundings for autonomous navigation.

Alongside Jetson TX2, Nvidia is also announcing JetPack 3.0, a new version of its AI SDK for the Jetson family, which includes support for TensorRT 1.0, cuDNN 5.1 for deep neural networks, VisionWorks 1.6 for computer vision, as well as all the latest graphics drivers and APIs.

Cisco say that it can use TX2 and Nvidias Jetson to add local AI-powered features including face and speech recognition to its Spark enterprise network devices, which could potentially offer a lot of advantages in terms of security and authentication. The TX2 is also set to help students and researchers do a lot more with a lot less investment than youd typically require for getting started with AI the developer kit for the new Jetson launches today, with a $599 price tag for preorders in the U.S. and Europe, and a ship date starting March 14.

Nvidias shipping TX2 module will retail for $399 when it arrives in Q2, and the existing TX1 and TK1 Jetson embedded computing platforms will also continue to be made available, at reduced prices.

A connected city is also going to have to be an intelligent city, and not just one where the smarts are centrally located in a server facility each component needs to be somewhat intelligent to help keep things running smoothly throughout the network, without having to worry about infrastructure continuity and latency concerns. Improvements like the generational jump for Jetson TX2 help make that potential future much more achievable.

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Nvidia's Jetson TX2 makes AI computing possible within cameras, sensors and more - TechCrunch

WHAT: "Trish," the washing machine-looking artificially intelligent spokesperson for Soylent.

WHO: Soylent, Wieden + Kennedy's The Lodge

WHY WE CARE: Soylent staked its ground in "this product is kind of creepy" territory from the moment they named their nutritional meal-replacement "soylent," after the 1973 horror-thriller Soylent Green. That film starred Charlton Heston as he attempted to discover what the processed meal replacement that people consumed was made of (spoiler: Soylent Green is people!!), which is not context you can avoid when discussing the product that bears its name.

However, if that alone isn't creepy enough for you, worry not: Now Soylent has teamed with agency Wieden+Kennedy to launch "Trish," an AI-powered spokesperson, who joins the company's sci-fi dystopian messaging like "Food That Frees You," and an eerily sterile design. According to press materials, Trish "is rational, cares about humans, knows a ton about nutrition, and above all elseis helpful." Certainly, "cares about humans" is high on the list of things that humans should look for in an AI buddy (it beats the alternative, anyway). And having a robot pal who can help customers aspiring not to eat actual meals with figuring out what to do with their tubes and/or powdered bags of sort-of food, how much to eat, what flavors they might enjoy, and more. If getting those answers from an AI bot that looks like a washing machine appeals to you, congratulations! You are definitely in Soylent's target demographic.

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Soylent Found A Way To Make Itself Even Creepier: An AI Spokesperson - Co.Create

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Salesforce has built in its Einstein artificial intelligence platform and the upshot is that the move appears to be a smart way to grow organically and hit its revenue growth targets.

How to Implement AI and Machine Learning

The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started.

The company hosted a strategy meeting for the fiscal year ahead and rolled out its Spring 2017 release, which integrated Einstein across its platform. Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, Analytics Cloud, and Community Cloud will all get Einstein integration and add-on features.

Meanwhile, Salesforce and IBM announced a partnership that will integrate Einstein with Watson. In other words, the two most marketing heavy artificial intelligence brands have teamed up. Watson has become a marketing juggernaut for IBM's future. It's safe to say that Salesforce will push Einstein on name recognition alone. Who doesn't want the Einstein name on its cloud?

Salesforce and IBM will integrate APIs and use IBM's BlueWolf consulting unit to combine Watson and Einstein. IBM will use Salesforce's Service Cloud. See: IBM, Salesforce announce AI partnership | Salesforce Einstein: Here's what's real and what's coming.

CEO Marc Benioff trotted out Amazon Web Services, a key customer for Einstein lead scoring and Salesforce. The two companies are partners. AWS marketing chief Ariel Kelman said it's early days, but the company plans to roll out Einstein lead scoring and other tools throughout its sales processes.

Benioff, who called Einstein "a new member on the management team," noted Einstein will evolve through multiple clouds. Salesforce also teased out the Summer release of its platform too. In a slide it's clear that there will be a lot more of Einstein.

Now it's clear that artificial intelligence will be critical, but it didn't take long for analysts to start pondering the financial ramifications. Macquarie Capital analyst Sarah Hindlian said in a research note:

Salesforce outlined Einstein customers such as Coca-Cola, AWS, Seagate, U.S. Bank, and Air France-KLM.

Lead scoring and processing may be strong enough to get customers to add Einstein to the mix. In the Salesforce pricing list, Einstein is typically denoted with a "$" to indicate an additional charge.

Here are a few screens that will tell the Einstein upsell tale.

Now what remains to be seen is how quickly customers take the Einstein add-ons, but it's likely more than a few will because enterprises aren't going to have the AI knowhow or talent base around. Wall Street expects Salesforce to deliver $10.18 billion in revenue in fiscal 2018, up from $8.39 billion for the just closed year. By fiscal 2021, Salesforce is expected to have revenue of $16.68 billion.

When does AI make sense for business?

More here:

Salesforce's Einstein: One smart way to upsell AI - ZDNet

This is the secondof three stories in a mini-series on how artificial intelligence is affecting the work that agencies do. The nextinstallment will publish on Friday. Read the first story about Xaxis.

As marketer interest in artificial Intelligence (AI) grows, Publicis.Sapient sees opportunity to provide guidance.

It has a dedicated unit that provides AI-related advice for 30 clients, including Patrn and Dove soap. The AI practice informally launched about four years ago and has seen an uptick in spend over the past year and a half, said Josh Sutton, global head of AI at Publicis.Sapient.

A year and a half ago, there was very minimal spend [in AI], he said. In 2017, companies are acknowledging that AI and its intersection with big data is going to enable business transformation in a fundamental way.

Sutton works with a team of about 50 AI specialists who help marketers select and deploy AI tools. That work involves setting initial expectations.

Theres a fairly wide gap in most peoples knowledge based on what theyve seen and read from academic journals to Hollywood movies, he said. Setting expectations around whats real is one of the most time-consuming and critical tasks.

For example, marketers worry that AIs ability to automate certain tasks will leave them jobless. In reality, AIs ability to automate more mundane, machine-like functions makes marketers more effective and efficient at their jobs and able to focus on more high-level work like strategy in order to achieve better results, Sutton said.

Marketers can use AI to create conversational tools like voice assistants and chatbots and accelerate automated tasks within their own organizations. In programmatic, marketers can tap machine learning and natural language processing to crunch massive data sets at the individual level for more targeted media buys.

Its a tremendously large data set that, prior to AI, you wouldn't have been able to do a ton with because it wouldve taken an army of data scientists to get the information out, Sutton said.

At that level of granularity, marketers can funnel their media spend toward channels where they know an individual will be to eliminate waste. Machine learning helped a major airline client recognize waste was coming from its out-of-home advertising and reduced that spend by 15% by targeting with more accuracy.

We could create a real persona of that individual and look at where they travel, what routes they take to drive to the airport and where they live, Sutton said. We knew exactly who we were going after.

Publicis.Sapients AI team also helps clients rethink their marketing strategies and shift media spend out of product and P&L silos and toward audience-based insights. The process can be bumpy and painful, Sutton said.

Its still an early-days transformation, he said. The majority are still centered around P&L lines.

Retail and finance have shown the most initiative embracing AI and audience-based buys, while CPGs and the heavily regulated health care vertical trail behind, Sutton said.

Often, Publicis.Sapients AI practice is pitted against consultancies rather than agencies in pitches, given the systems integration and technology expertise required to deploy AI tech.

The AI Players

While the group is AI-agnostic, Publicis.Sapient works most often with the biggest players, including Google, Microsoft and IBM.

IBM has set the vision for whats possible with AI, but its advertising is a bit more aspirational than its product as an enterprise solution, Sutton said. Google, on the other hand, has great technology but isnt communicating how it can be deployed at the enterprise level. And Microsoft has quietly become to go-to solution for specific verticals.

If I was going to put my chips down on a few companies today, [IBM, Google and Microsoft are] where theyd be, he said.

Salesforce and Amazon are up-and-comers. Sutton sees the former becoming more of a niche play rather than an enterprise solution and the latter expanding on its great work in experience design with Echo.

But a clients AI stack often involves bits and pieces of integrated technologies. Publicis.Sapient keeps an ear to the ground for smaller players with better point solutions than the big guys.

Locking yourself into one partner is very dangerous, he said. I have concern with companies that are unwilling to be part of a broader enterprise infrastructure because I have yet to see anyone who can put all the pieces together. If the Googles, IBMs and Microsofts arent there yet, I struggle to think anyone else will be.

Even with the bigger players, Sutton and his team do their due diligence to make sure the technologies they select are easy to use, scalable and have worked in the past for brands.

This is an industry where unfortunately the gap between the hype and reality of what a product can deliver is fairly wide, he said. Even in the forgiving realm of technology overstatement, the AI space has taken it to a new level.

While Sutton declined to share exact numbers, he said Publicis Groupe will likely invest millions in AI this year both in its client-facing AI practice and to optimize the core functions within its own business, including media planning and buying.

And as with most centralized functions in agency holding groups, AI is beginning to trickle down into Publicis.Sapients agency brands to become core to their individual capabilities.

It gets very fuzzy as to who are or arent pure AI team members as we start to scale out, Sutton said.

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AI And The Agency: How Publicis.Sapient Helps Marketers Navigate AI - AdExchanger

Another legal tech startup coming out of the UK: Cognitiv+is applying artificial intelligence to automate contract analysis and management, offering businesses a way to automate staying on top of legal risks, obligations and changing regulatory landscapes.

Co-founder Vasilis Tsolis might therefore be forgiven for viewingBrexit as a sizableopportunity for his startup though he more tactfully describes it as a legislative challenge that we can help out with.

Theres going to be a lot of changes in legislation, theres going to be a lot of changes in regulation,and you really need to know whats going to happen to your contracts and if you need to do any changes on your legal documents or not. So its going to be a huge challenge, he says of Brexit.

I think this is going to happen more and more often, he adds, pointing to another incoming EU regulation that will be upping businesses compliance needs in the near future: aka the GDPR, coming into force (including in the UK) in May 2018.

Because you see legislation changing so fast and its getting so much bigger that actually its impossible to monitor and impossible to read it. Who can read half million of pages?

This is about day-to-day contract management but we think that compliance is going to be more and more strict, and its going to be much more difficult there are so many new regulations, about Slavery Act, about GDPR, MiFid II and so many other compliances that all thisaccumulated risk analysis from your contracts we think its not possible to be viable for humans anymore you need to bring the robots, he adds.

Cognitiv+s data-parsing toolis not being designed to interpret legislation but rather to monitor it in a structured fashion, combining that tracking with analysis of a companys own contracts with a view to flagging compliance risks and requirements.

The overarching thesis is that contract analysis space/legal process outsourcing has yet to be disrupted by technology.Tsolishas both an engineering and a legal background, as youd hope give the natureof the startup. Other co-founder, Achilleas Michos, background is in computer science.

When you do contract analysis and compliance and when you do regulatory analysis theres a lot of repetition, and the majority of the people spend a lot of time perhaps the majority of the time looking for basic stuff Legal but also admin assignments. So the majority of those tasks can be accelerated by automation, argues Tsolis.

Cognitiv+ is using what he describes as a number of AI technologies to perform the contract analysis at near real-time speeds, leaning on open source algorithms for the coretech. But hedescribes theIP as the process and all the stages we take for analyzing a contract, the training so, in other words,the legal expertise needed to get a proper handle on compliance.

We use machine learning, we use NLP [natural language processing], we use neural networks We aim to be a risk management tool; we identify as much as possible that the machine can do, he tells TechCrunch. When it comes to [analyzing an area such as] limit of liability if the contract is not very well drafted then obviously we cannot help you on this one but we can help you for the vast majority of the contracts that you have on your library.

Presumably the techmight also be able to flag up a badly drafted contract.

Contracts areuploaded to thesystem for analysis and tracking, with examples of the sorts ofcritical information Cognitiv+ can extract including the parties of the contract; the limit of liability; renewal and termination information; and jurisdiction.

Users are deliveredintel on an ongoing basis viareports, dashboards and notifications. The tool is generally being designed for use by in-house lawyers, commercial staff, procurement, financial and compliance departments.

The current industryfocus for the team is procurement in the financial sector, but next year itplans to expand to target the insurance, real estate and engineering industries too. Theyre currently also only tracking UK-related compliance, but are intending to add EU and US in the coming months.

Given the financial services focus, theyre alsolooking at how thetech could be used tohelp combat financial crime, according to Tsolis.

The early stage startup has been bootstrapping since being founded in late 2015, and has just gone through Londons Winton Labs accelerator for data-focused businesses. Its also in the midst of closing a seed round, and is running pilots of v1 of itscontract-parsing platform with a small number of UK companies.

Tsolis says the first version is a fairly generic analytical tool, but a more vertical-specific v2 is coming in September forfinancial and procurement users and anotherversion planned for March 2018 will target the other three target sectors. The aimisto begin revenue generating this year, via a SaaS business model.

Could the tech also be applied for drafting contracts in future, not just analyzing them? UK startup Juro, for example, already offers both contract authoring and management, though itlooks to have a bit of a different focus(on marketplaces and sales contracts).

The legal world is a long livedworld for centuries now, and its a very traditional sector. And actually I think you need to disrupt it step by step, saysTsolis, emphasizing the need tobring together all the stakeholders to ensure buy-in.

Its not just the lawyers a lot of people read contracts, like the financial people, commercial, procurement, compliance, so you need to bring all the stakeholders together to ensure that people understand what the machine does. And move on to new ways of interacting with the machine and NLP and new technologies.

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Cognitiv+ is using AI for contract analysis and tracking - TechCrunch

Research works best when it takes account of multiple views by Vincent Conitzer / March 6, 2017 / Leave a comment

Are driverless cars the future Fabio De Paola/PA Wire/PA Images

Progress in artificial intelligence has been rapid in recent years. Computer programs are dethroning humans in games ranging from Jeopardy to Go to poker. Self-driving cars are appearing on roads. AI is starting to outperform humans in image and speech recognition.

With all this progress, a host of concerns about AIs impact on human societies have come to the forefront. How should we design and regulate self-driving cars and similar technologies? Will AI leave large segments of the population unemployed? Will AI have unintended sociological consequences? (Think about algorithms that accurately predict which news articles a person will like resulting in highly polarised societies, or algorithms that predict whether someone will default on a loan or commit another crime becoming racially biased due to the input data they are given.)

Will AI be abused by oppressive governments to sniff out and stifle any budding dissent? Should we develop weapons that can act autonomously? And should we perhaps even be concerned that AI will eventually become superintelligentintellectually more capable than human beings in every important waymaking us obsolete or even extinct? While this last concern was once purely in the realm of science fiction, notable figures including Elon Musk, Bill Gates, and Stephen Hawking, inspired by Oxford philosopher Nick Bostroms Superintelligence book, have recently argued it needs to be taken seriously.

These concerns are mostly quite distinct from each other, but they all rely on the premise of technical advances in AI. Actually, in all cases but the last one, even just currently demonstrated AI capabilities justify the concern to some extent, but further progress will rapidly exacerbate it. And further progress seems inevitable, both because there do not seem to be any fundamental obstacles to it and because large amounts of resources are being poured into AI research and development. The concerns feed off each other and a community of people studying the risks of AI is starting to take shape. This includes traditional AI researchersprimarily computer scientistsas well as people from other disciplines: economists studying AI-driven unemployment, legal scholars debating how best to regulate self-driving cars, and so on.

A conference on Beneficial AI held in California in January brought a sizeable part of this community together. The topics covered reflected the diversity of concerns and interests. One moment, the discussion centred on which communities are disproportionately affected by their jobs being automated; the next moment, the topic was whether we should make sure that super-intelligent AI has conscious experiences. The mixing together of such short- and long-term concerns does not sit well with everyone. Most traditional AI researchers are reluctant to speculate about whether and when we will attain truly human-level AI: current techniques still seem a long way off this and it is not clear what new insights would be able to close the gap. Most of them would also rather focus on making concrete technical progress than get mired down in philosophical debates about the nature of consciousness. At the same time, most of these researchers are willing to take seriously the other concerns, which have a concrete basis in current capabilities.

Is there a risk that speculation about super-intelligence, often sounding like science fiction more than science, will discredit the larger project of focusing on the societally responsible development of real AI? And if so, is it perhaps better to put aside any discussion of super-intelligence for now? While I am quite sceptical of the idea that truly human-level AI will be developed anytime soon, overall I think that the people worried about this deserve a place at the table in these discussions. For one, some of the most surprisingly impressive recent technical accomplishments have come from people who are very bullish on what AI can achieve. Even if it turns out that we are still nowhere close to human-level AI, those who imagine that we are could contribute useful insights into what might happen in the medium-term.

I think there is value even in thinking about some of the very hard philosophical questions, such as whether AI could ever have subjective experiences, whether there is something it would be like to be a highly advanced AI system. (See also my earlier Prospect article.) Besides casting an interesting new light on some ancient questions, the exercise is likely to inform future societal debates. For example, we may imagine that in the future people will become attached to the highly personalised and anthropomorphised robots that care for them in old age, and demand certain rights for these robots after they pass away. Should such rights be granted? Should such sentiments be avoided?

At the same time, the debate should obviously not exclude or turn off people who genuinely care about the short-term concerns while being averse to speculation about the long-term, especially because most real AI researchers fall in this last category. Besides contributing solutions to the short-term concerns, their participation is essential to ensure that the longer-term debate stays grounded in reality. Research communities work best when they include people with different views and different sub-interests. And it is hard to imagine a topic for which this is truer than the impact of AI on human societies.

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The AI debate must stay grounded in reality - Prospect

At any given moment, you likely have dozens of marketing emails sitting in your inbox. "HUGE sale ends TODAY." "Get yours now!" "SALE!" It's as though your email is filled with dozens of desperate salespeople, all clamoring for your attention. But there isn't necessarily a human behind them at all. There's a good chance that some of these emails were generated by an algorithm that deploys individualized phrases based on what kinds of emotional pleas work best on you.

That's what Persado does. The startup uses an algorithm to analyze a company's audience down to the individual level, paying attention to what you've clicked on from that branddata thats already collected by the company and anonymized before it reaches Persadoand what emotional phrases are most likely to catch your attention. Are you attracted to words that indicate exclusivity? Or do urgent messages tend to catch your eye? Persado takes all that data and uses another machine learning algorithm to generate messages that may be more likely to make you click.

It's a glimpse of the kind of personalization of language that could transform UX over the next few years, as AI becomes an integral part of research and design. And Persado is experimenting with real-world applications, applying what it's learning to the real world, in use cases like subway PA announcements. The technology nods toward a sensor-filled future where individually targeted messages transcend the digital world and follow us into meatspace.

Persado's data shows that adding emojis to email subject lines can increase click rates.Image: Persado

Though email marketing is its bread and butter, Persados AI isn't just in your inbox. The companys algorithms also write copy for text messages, advertisements across many platforms, landing pages, social media posts, and push notifications, which it says adds up to 2 billion impressions per month, for clients that range from Fortune 100 companies like Verizon, Microsoft, and American Express to household brands like Overstock.com, Kmart, Saks Fifth Avenue, Expedia, Sirius XM, and fantasy sports platform Draft Kings. In one campaign with the clothing retailer Lucky Brand, conversion rates increased by 127%. An anonymous case study with a Fortune 200 credit card company increased conversion rates by 410%.

Assaf Baciu, cofounder and SVP of product at Persado, says that the company is bringing the nuance of individual human communication back to mass marketing. "If we were face-to-face, I would strive to get signals to see if my message works, and I adjust the message so it hopefully inspires you to act," he says. For companies trying to reach consumers, it can be tough to gauge the efficacy of its messages, or how they compare to subtle variations.

Persados technology plays on a fundamental truth of design with AI: That it should excel where humans tend to fail. "Writing messages day in, day out, and analyzing the signals of the feedback, is impossible to do for humans," he says. "The machine can do that."

Persado's algorithm parses millions of marketing phrases into five key emotionspride, trust, anticipation, joy, and fear. Each of those is subdivided into three more emotional subclasses, each of which can be used to create messages targeted at individuals.Image: Persado

Heres how the system works. The algorithm was trained on the language of email campaigns, web pages, and search ads, each of which was broken down into variables: the product or offer description ("these shoes are on sale!"), the formatting (including capitalization, fonts, and emojis), the structure (paragraph, bullet points, and verb tense), the call to action ("buy this!" or "click here!"), and, most importantly, the emotional language. Using social psychology research around emotions, Baciu and his team identified five primary emotions that motivate people to clickjoy, pride, trust, anticipation, and fearand three emotional subcategories between each one. Each marketing phrase was tagged with the appropriate emotions, and the algorithm was trained to recognize the emotional intent of phrases using this data set.

By combining the trained algorithm with a client's existing data about how their users have interacted with communications in the past, and testing different types of language on these users, Persado builds profiles that identify which emotions convince users most effectively. Then the company can use its second algorithm to piece together emotionally charged language that effectively targets messages to users based on their behavior in the past.

The key to all of this is data. The algorithm can't simply generate "better" language for any old message, because it needs data about what a particular audience tends to engage with. "AI without context does not really work," Baciu says. "There is no generic AI. We are still defining our knowledge with every campaign."

This limitation has kept Persado squarely in the digital marketing industry, but Baciu says the company has aspirations that cross over into product design and user experience. Baciu posed two examples: What if your Fitbit knew exactly what to say on a particular day to motivate you to get off the couch and run a 5K? Or what if pharmaceutical companies or doctors could use an algorithm to individually target messages to users who haven't taken their prescription drugs that day?

More emotionally charged language in this email subject line led to a 63% increase in click rate. Clearly no one wants to click on a pun as bad as this one.Image: Persado

Persado is actively experimenting outside of marketing. The company recently completed a similar internal experiment on New York's MTA transit system, rewriting the audio messages that notify riders that their train has been stopped by traffic ahead, or reminding them to not lean against or block the subway doors, and applying what it has learned about effective messaging to make these often annoying notifications a little more engagingeven pleasant. According to CityLab, the company changed the classic "Stand clear of the closing doors, please" to "Please be careful of the closing doors," because adding politeness to the front of the phrase is nicer for listeners. "Stand clear," which is apparently "technically worded," is replaced with "be careful," which is clearer, conveys importance, and is more emotionally resonant.

It was purely an internal experiment, and while Persado says the MTA does know about its existence, they're unaware if the MTA will use the new messages or not. The company has no way of testing whether these changes are actually more effective, since it cant carry out controlled A/B testing on one of the busiest subway systems in the world. But it hints at how optimizing language itself, based on troves of existing data, could manipulate listeners to behave differently.

To demonstrate, Co.Design asked Persado to try rewriting two possible headlines for this article using its AI. But again, since the copy doesn't fit the normal use-case for the algorithm and it couldnt test it using any data about Co.Design's audience, we had to settle for headlines that were informed by the company's copywriting experience. One potential headline, "This Algorithm Tailors The Web To Your Personality," became "Whoa . . . This Algorithm Knows Exactly What Makes You Click." Persado told Co.Design in an email that this language taps into the emotion of "exclusivity." And as for the "whoa"? Persado says that "our data shows that adding brief, introductory languagein this case conveying excitementcan set a more emotional tone and draw attention to what comes after."

The company transformed another potential headline, "Made You Click: The AI At Work In Your Inbox," into "Made You Click 😉 Were Letting You In On The Secret AI Behind Your Inbox." Yes, that's a smiley face. Persado claims its data shows that the 😉 symbol "outperform[s] other variants 79% of the time in editorial campaigns."

Tapping into "achievement" emotional language drastically increased the percentage of people clicking on this email.Image: Persado

Both the MTA and Co.Design headline experiments demonstrate some of the hurdles Persado needs to clear before it can use its technology in broader applications. In terms of MTA, there's no way to know if Persado's language would actually make frustrated New York subway riders less angstyespecially when the MTA lacks the infrastructure to deliver personal messages to each rider. And per the headline experiment, it's unclear if Persado's emoji-fied, clickbait-y headlines would drive readers away in the long term, making them unsuitable for use in media organizations without a human editor to proactively make that decision.

More broadly, these factors are whats stopping the company from moving from digital promotional messaging to language in offline user experience, whether that's in the subway or in a physical store. First, there's a lack of physical infrastructure that would permit the company to truly individualize its messages. But beyond that, there's a lack of clean, objective data about how users are reacting to stimuli in the real world, making it difficult to train an algorithm to generate language or conduct experiments to see what kind of messaging is most effective. Persado's algorithms need data to learn, and a control audience on which to test its ideas.

Yet more and more "smart" objects are colonizing our world, tracking their owners and harvesting data about their behavior. That includes cities, which are using increasingly connected systems to test and manipulate citizen behavior. "If the purpose was to get the trash in the trash can, we could probably work on that, assuming we can measure how many people actually put the trash in the can," Baciu says. "Connectedness allows AI to emerge across many industries."

Persado is firm that it wants to use its algorithm to generate promotional content that's simply more in tune with human emotions, but its business points to the reemergence of language as a vitally important part of UX design, through AI-generated messages that are personalized to each person who looks at them. While chatbot-style applications promised to tailor communication to each user, this technology could be embedded seamlessly across platforms, whether through an app or a verbal interface like Google Home.

The potential of similar technology down the road could be powerful, and even a bit unsettling. Its easy to imagine a more ominous vision of the future hereone where every piece of language you see, whether it's on a store sign or an app, is tailored to your personality to convince you to buy, a la Minority Report. If your phone knows how you're feeling at all times, every bit of language it broadcasts to you could be tweaked to suit your mood and capitalize on your emotions. It would mean mass manipulation on an unprecedented levelespecially if these tactics aren't disclosed to the consumer.

So next time you take a stroll through the torrents of promotional emails sitting in your inbox and you find yourself drawn to certain ones over others, remember: An algorithm may have made you click.

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Made You Click: Meet The AI Lurking In Your Inbox - Co.Design (blog)

Artificial intelligence, with the human touch, is building a new customer experience

Artificial Intelligence is the definitive technology of the 21st century. All businesses of every size, in every industry will be impacted by AI. In the age of the connected customer, every 1 in 5 U.S. adults are almost never offline, customer experience is the battleground for true differentiation. Today, every successful consumer application is powered by AI. Tomorrow, every successful business will be powered by AI. The line-of-business that is most likely to embrace AI first will be the customer service typically the most process oriented and technology savvy organization within most companies. But before we dig into AIs tremendous potential in transforming customer service, lets scope AIs market size and growth projections.

The Artificial Intelligence (AI) Market Size and Future Projections

Today, 38% of enterprises are already using artificial intelligence (AI), growing to 62% by 2018. Forrester is predicting a 300% increase in AI investments in 2017 compared to 2016 and IDC believes AI will be a $47 billion market by 2020. Forrester lists the top 10 AI technologies here:

Forrester

Gartner named Intelligence Applied AI and Advance Machine Learning, Intelligent Apps, and Intelligent Things as 3 of its top 10 strategic technology trends for 2017.

The disruptive power of AI will impact every business, in every industry. According to Gartner, by 2020, 20% of companies will dedicate workers to monitor and guide neural networks. Gartner advises CIOs to look at areas of the company that have large data sets but lack analytics. AI can provide augmented intelligence with respect to discovery, predictions, recommendations and automation at scale.

PWC named Artificial Intelligence (AI) as one of the eight essential technologies in business. Today, there are 1,652 artificial intelligence (AI) startups and private companies that have captured over $12.24 billion of funding.

Venture Scanner

The power of artificial intelligence is mass personalization and contextual intelligence at scale. According to Accentures 2017 Technology Vision Report, AI could double annual economic growth rates by 2035. Accenture also notes that AI is the new UI. AI is becoming the new user interface (UI), underpinning the way we transact and interact with systems. Seventy-nine percent of business leaders agree that AI will revolutionize the way they gain information from and interact with customers. As AI takes over more of the user experience, it grows beyond just an intelligent interface. With each customer interaction becoming more personalized, powerful, and natural, AI moves into an even more prominent position: your digital spokesperson, Accenture Technology Vision 2017

AI Implementation Realities in the Enterprise

Although Amazons Alexa, has evolved from 1,000 voice command interpretations (or skills) to now more than 10,000 skills in one year period, there is still a lot of AI progress to be made before machines can truly understand and guide next best actions.

Robots, AI will replace 7% of US jobs by 2025 Forrester. Here are the highlights of the report:

The future of work projections and AIs impact on jobs may appear aggressive and somewhat unrealistic. In order to better understand the realities of AI in business, it is important to define phases and prerequisite of AI deployments in large businesses.

The three key phases of enterprise AI roll out: data, algorithms, and workflows. The power of AI usefulness is a function of the quality and quantity of data. Algorithms will help deliver insights discovery, prediction, recommendation and automation of existing manual processes require strong, self-learning and adaptable algorithms. The final phase and the most challenging is the workflows. The constant iteration of analyzing data, researching and developing algorithms, and creating timely actions based on gleaned insights through robust workflows is the job of data scientists and line-of-business people experts. Workflows that guide customer engagements must not be automated to a point where businesses lose sight on the importance of the human touch, empathy and relationship building aimed at earning the right to be a trusted advisor and strategic business partner.

For most companies, the algorithms and workflow complexities will the use of augmented intelligence. This is especially true for complex customer relationship management workflows in B2B customer service functions. That said, there is exponential growth in AI innovation and advancements and companies cannot afford to tag AI as hype, only to find themselves significantly behind their competitors in 1-2 years. AI knowledge, planning and adoption must happen now.

The Role of AI in Customer Service

Today machines have the ability to interact with humans at a level that used to only seem possible in sci-fi movies. Amazon serves up personalized product recommendations, Facebook automatically tags photos and Google maps proactively reroutes you around traffic. AI is powering nearly every experience we have-- making it smarter, seamless and personalized-- and as a result our expectations as consumers are at an all-time high. The most indispensable consumer apps are powered by AI technologies, delivering real personalized value, in real-time. This seamless personalized, immediate and intelligent user experience will make its way to every business, across all industries. AI in business will create motion and flow-based solutions and services. In order for customer service leaders to stay relevant, they must think differently and educate their stakeholder about AI. AI allows companies to deliver these smarter, more personalized and predictive experiences that customers have come to expect, but the human touch is still table stakes for customer success. The most suitable line-of-business to start with AI? Customer Service.

According to Salesforce research, 92% percent of senior executives believe that customer experience is a key competitive differentiator and they view customer service as the primary vehicle for improving the customer experience. In order for customer service organizations to lead customer experience transformation, they must fully embrace, deploy and utilize AI technologies.

What does excellent customer service look like? According to research, excellent customer service is personalized, always on and real-time, consistent and omni-channel. To achieve customer service excellence, service organization must leverage AI to bolster their discovery, prediction, recommendation and automation engines.

Salesforce Research

In the age of the customer, contact channels are expanding rapidly and the amount of data created - both structured and unstructured means that service organizations are drowning in data, but starving for actionable insights.

Salesforce Research

Forrester identifies extended and enhanced self-service, powered by AI technologies as one of its top trends for customer service in 2017. Customer service will continue to invest in structured knowledge management and leverage communities to extend the reach of curated content. Service will become more ubiquitous, via speech interfaces, devices with embedded knowledge, and wearables for service technicians, said Kate Leggett. The second top trend is sustained customer conversations using natural language processing technology. Companies will continue to explore the power of intelligent agents to add conversational interfaces to static self-service content. They will anticipate needs by context, preferences, and prior queries and will deliver proactive alerts, relevant offers, or content, said Leggett.

Top service teams are 3.9X more likely than underperforming service organizations to say predictive intelligence will have transformational impact on their customer service by 2020. The common theme that I hear as I collaborate with business leaders is that AI biggest potential is to augment our ability to connect with customers and giving way to a smarter customer experience.

Salesforce

With projections of 6 billion smartphone users and over 50 billion connected devices by 2020, the next generation customer experience will be powered by artificial intelligence. A CRM platform powered by AI will analyze customer engagements and automatically predict sentiment and adjust customer journeys to ensure optimal user experience. The same logic applied to prediction marketing lead scores and sales opportunity conversions will be applied to customer services cases, optimizing time-to-resolution cycles and improved customers satisfaction and net promoter scores (NPS).

Service organizations can significantly improve the customer experience and bolster service delivery capacities using AI technologies. Service managers using AI can gain real-time insights across all customer contact channels with AI-powered analytics to increase team productivity and CSAT. By using smart data discovery, service managers can optimize agent availability, wait times and opportunities for proactive service delivery. Using machine learning, cases are automatically escalated and classified using sensitivity and domain expertise predictive analytics. AI powered chat bots can deliver knowledge using automated workflows. Field service professionals can use mobile apps powered by AI, delivering precision service based on access to CRM data that can deliver personalized services anywhere. AI powered field apps use algorithms to optimize scheduling and routing using complete CRM data.

Salesforce Research

The top trends for CRM in 2017 includes intelligence powering prescriptive advice, according to Forrester.

The advantage of a CRM platform powered by artificial intelligence goes far beyond just the services organization. At Salesforce, the artificial intelligence technology, called Einstein, is infused across all the Salesforce clouds, giving over 150,000 companies that use Salesforce to seamlessly access AI capabilities across their sales, services, marketing, IT and community organizations. Marketers using AI have seen an average 25% lift in click through and opens. Sales professionals using AI predictive lead scoring have a 300% increase in lead to opportunity conversions. Commerce teams using AI have 7-15% increase in revenue per site visitor.

Artificial Intelligence is the definitive technology for the 21st century, and companies that use AI as augmented intelligence to make more informed and faster decisions will win the age of the customer where personalization, immediacy and intelligence are the new currencies of growing businesses. But to sustain growth and earn customers trust, businesses have to use common sense, care more and be cautious of over-automating. Businesses must practice empathy, inside and outside of the company, and deliver on their promises.

More:

AI-Powered Customer Service Needs The Human Touch - Huffington Post

Amazon.com, Inc. (NASDAQ:AMZN) has been making quite a push into the field of artificial intelligence (AI). You are no doubt familiar with the most public example of this effort -- Alexa, its voice-activated digital assistant which controls the Echo smart speaker and Echo Dot, which were top sellers on Amazon's website over the holidays. Those familiar with Amazon Web Services (AWS), an industry leader in cloud computing, may also be aware of the AI-based tools the company has recently made available to AWS customers: Rekognition for building image recognition apps; Polly for translating text to speech; and Lex, to build conversational bots.

Image source: Pixabay.

What you may not know is that Amazon is adding cyber-security to its AI resume. TechCrunch is reporting that Amazon has acquired AI-based cyber-security company Harvest.ai. According to its website, Harvest.ai uses AI-based algorithms to identify the most important documents and intellectual property of a business, then combines user behavior analytics with data loss prevention techniques to protect them from cyber attacks. Harvest.ai already had ties to Amazon, as a customer who was featured in an AWS Startup Spotlight article, which focuses on innovative and disruptive young companies. Harvest.ai boasts former members of the National Security Agency (NSA), Federal Bureau of Investigation (FBI), Department of Defense (DoD), as well as former employees of Websense and FireEye, Inc.

Harvest.ai's flagship product, MACIE, monitors a company's network in near real-time to identify when a suspicious user accesses unauthorized documents.Its target market was "Fortune 1000 organizations that were migrating to cloud-based platforms." Amazon has a Who's Who of big name companies as customers, so it seems like a natural fit for the company. If it decides to deploy MACIE to its cloud, it adds to the suite of hosting products available for its customers.Amazon already offers its Amazon Inspector, which it defines as an "automated security assessment service to help improve the security and compliance of applications deployed on AWS."Harvest.ai would take that to the next level.

AI is perfectly suited for the task of cyber-security. Image source: Pixabay.

The use of AI in cybersecurity isn't new. MIT has been experimenting with a novel approach to application. By pairing a system with a human counterpart and applying supervised learning, the system was able to detect 85% of threats. Over time, that success rate is sure to improve. Last year, IBM (NYSE:IBM) announced an initiative to train its AI-based Watson in security protocols, in what was to be a year-long research project. By the end of the year, the company expanded the beta program with the inclusion of 40 clients across a variety of industries. Earlier this month, IBM announced that Watson for Cyber Security would be available to customers.

The task of cyber security seems ideally suited to AI applications. The ability to digest a magnitude of data in a short time and match real-time situations against a set of specified criteria seems tailor made for the platform. Add to this AI's ability to learn over time and it seems inevitable that there would be a merging of these technologies.

These acquisitions combined with Amazon's own research makes it one of several companies on the cutting edge of AI. Amazon has been applying the knowledge it gains across a wide swath of its business from consumer facing products to its business-centric applications. Amazon's investors are sure to benefit from this multi-pronged approach.

Danny Vena owns shares of Amazon. The Motley Fool owns shares of and recommends Amazon and FireEye. The Motley Fool has a disclosure policy.

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Amazon's Latest AI Push: Cybersecurity - Motley Fool

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