Schlagwort-Archive: AI

Hey Alexa, What Are You Doing to My Kid’s Brain?

Kommentar verfassen

“Unless your parents purge it, your Alexa will hold on to every bit of data you have ever given it, all the way back to the first things you shouted at it as a 2-year-old.”

Among the more modern anxieties of parents today is how virtual assistants will train their children to act. The fear is that kids who habitually order Amazon’s Alexa to read them a story or command Google’s Assistant to tell them a joke are learning to communicate not as polite, considerate citizens, but as demanding little twerps.

This worry has become so widespread that Amazon and Google both announced this week that their voice assistants can now encourage kids to punctuate their requests with „please.“ The version of Alexa that inhabits the new Echo Dot Kids Edition will thank children for „asking so nicely.“ Google Assistant’s forthcoming Pretty Please feature will remind kids to „say the magic word“ before complying with their wishes.

But many psychologists think kids being polite to virtual assistants is less of an issue than parents think—and may even be a red herring. As virtual assistants become increasingly capable, conversational, and prevalent (assistant-embodied devices are forecasted to outnumber humans), psychologists and ethicists are asking deeper, more subtle questions than will Alexa make my kid bossy. And they want parents to do the same.

„When I built my first virtual child, I got a lot of pushback and flak,“ recalls developmental psychologist Justine Cassell, director emeritus of Carnegie Mellon’s Human-Computer Interaction Institute and an expert in the development of AI interfaces for children. It was the early aughts, and Cassell, then at MIT, was studying whether a life-sized, animated kid named Sam could help flesh-and-blood children hone their cognitive, social, and behavioral skills. „Critics worried that the kids would lose track of what was real and what was pretend,“ Cassel says. „That they’d no longer be able to tell the difference between virtual children and actual ones.“

But when you asked the kids whether Sam was a real child, they’d roll their eyes. Of course Sam isn’t real, they’d say. There was zero ambiguity.

Nobody knows for sure, and Cassel emphasizes that the question deserves study, but she suspects today’s children will grow up similarly attuned to the virtual nature of our device-dwelling digital sidekicks—and, by extension, the context in which they do or do not need to be polite. Kids excel, she says, at dividing the world into categories. As long as they continue to separate humans from machines, she says, there’s no need to worry. „Because isn’t that actually what we want children to learn—not that everything that has a voice should be thanked, but that people have feelings?“

Point taken. But what about Duplex, I ask, Google’s new human-sounding, phone calling AI? Well, Cassell says, that complicates matters. When you can’t tell if a voice belongs to a human or a machine, she says, perhaps it’s best to assume you’re talking to a person, to avoid hurting a human’s feelings. But the real issue there isn’t politeness, it’s disclosure; artificial intelligences should be designed to identify themselves as such.

What’s more, the implications of a kid interacting with an AI extend far deeper than whether she recognizes it as non-human. „Of course parents worry about these devices reinforcing negative behaviors, whether it’s being sassy or teasing a virtual assistant,” says Jenny Radesky, a developmental behavioral pediatrician at the University of Michigan and co-author of the latest guidelines for media use from the American Academy of Pediatrics. “But I think there are bigger questions surrounding things like kids’ cognitive development—the way they consume information and build knowledge.”

Consider, for example, that the way kids interact with virtual assistants may not actual help them learn. This advertisement for the Echo Dot Kids Edition ends with a girl asking her smart speaker the distance to the Andromeda Galaxy. As the camera zooms out, we hear Alexa rattle off the answer: „The Andromeda Galaxy is 14 quintillion, 931 quadrillion, 389 trillion, 517 billion, 400 million miles away“:

To parents it might register as a neat feature. Alexa knows answers to questions that you don’t! But most kids don’t learn by simply receiving information. „Learning happens happens when a child is challenged,“ Cassell says, „by a parent, by another child, a teacher—and they can argue back and forth.“

Virtual assistants can’t do that yet, which highlights the importance of parents using smart devices with their kids. At least for the time being. Our digital butlers could be capable of brain-building banter sooner than you think.

This week, Google announced its smart speakers will remain activated several seconds after you issue a command, allowing you to engage in continuous conversation without repeating „Hey, Google,“ or „OK, Google.“ For now, the feature will allow your virtual assistant to keep track of contextually dependent follow-up questions. (If you ask what movies George Clooney has starred in and then ask how tall he his, Google Assistant will recognize that „he“ is in reference to George Clooney.) It’s a far cry from a dialectic exchange, but it charts a clear path toward more conversational forms of inquiry and learning.

And, perhaps, something even more. „I think it’s reasonable to ask if parenting will become a skill that, like Go or chess, is better performed by a machine,“ says John Havens, executive director of the the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. „What do we do if a kid starts saying: Look, I appreciate the parents in my house, because they put me on the map, biologically. But dad tells a lot of lame dad jokes. And mom is kind of a helicopter parent. And I really prefer the knowledge, wisdom, and insight given to me by my devices.„

Havens jokes that he sounds paranoid, because he’s speculating about what-if scenarios from the future. But what about the more near-term? If you start handing duties over to the machine, how do you take them back the day your kid decides Alexa is a higher authority than you are on, say, trigonometry?

Other experts I spoke with agreed it’s not too early for parents to begin thinking deeply about the long-term implications of raising kids in the company of virtual assistants. „I think these tools can be awesome, and provide quick fixes to situations that involve answering questions and telling stories that parents might not always have time for,“ Radesky says. „But I also want parents to consider how that might come to displace some of the experiences they enjoy sharing with kids.“

Other things Radesky, Cassell, and Havens think parents should consider? The extent to which kids understand privacy issues related to internet-connected toys. How their children interact with devices at their friends‘ houses. And what information other family’s devices should be permitted to collect about their kids. In other words: How do children conceptualize the algorithms that serve up facts and entertainment; learn about them; and potentially profit from them?

„The fact is, very few of us sit down and talk with our kids about the social constructs surrounding robots and virtual assistants,“ Radesky says.

Perhaps that—more than whether their children says „please“ and „thank you“ to the smart speaker in the living room—is what parents should be thinking about.

Source:
https://www.wired.com/story/hey-alexa-what-are-you-doing-to-my-kids-brain/

Lawmakers, child development experts, and privacy advocates are expressing concerns about two new Amazon products targeting children, questioning whether they prod kids to be too dependent on technology and potentially jeopardize their privacy.

In a letter to Amazon CEO Jeff Bezos on Friday, two members of the bipartisan Congressional Privacy Caucus raised concerns about Amazon’s smart speaker Echo Dot Kids and a companion service called FreeTime Unlimited that lets kids access a children’s version of Alexa, Amazon’s voice-controlled digital assistant.

“While these types of artificial intelligence and voice recognition technology offer potentially new educational and entertainment opportunities, Americans’ privacy, particularly children’s privacy, must be paramount,” wrote Senator Ed Markey (D-Massachusetts) and Representative Joe Barton (R-Texas), both cofounders of the privacy caucus.

The letter includes a dozen questions, including requests for details about how audio of children’s interactions is recorded and saved, parental control over deleting recordings, a list of third parties with access to the data, whether data will be used for marketing purposes, and Amazon’s intentions on maintaining a profile on kids who use these products.

In a statement, Amazon said it „takes privacy and security seriously.“ The company said „Echo Dot Kids Edition uses on-device software to detect the wake word and only the wake word. Only once the wake word is detected does it start streaming to the cloud, and it will present a visual indication (the light ring at the top of the device turns blue) to show that it is streaming to the cloud.“

Echo Dot Kids is the latest in a wave of products from dominant tech players targeting children, including Facebook’s communications app Messenger Kids and Google’s YouTube Kids, both of which have been criticized by child health experts concerned about privacy and developmental issues.

Like Amazon, toy manufacturers are also interested in developing smart speakers that would live in a child’s room. In September, Mattel pulled Aristotle, a smart speaker and digital assistant aimed at children, after a similar letter from Markey and Barton, as well as a petition that garnered more than 15,000 signatures.

One of the organizers of the petition, the nonprofit group Campaign for a Commercial Free Childhood, is now spearheading a similar effort against Amazon. In a press release Friday, timed to the letter from Congress, a group of child development and privacy advocates urged parents not to purchase Echo Dot Kids because the device and companion voice service pose a threat to children’s privacy and well-being.

“Amazon wants kids to be dependent on its data-gathering device from the moment they wake up until they go to bed at night,” said the group’s executive director Josh Golin. “The Echo Dot Kids is another unnecessary ‘must-have’ gadget, and it’s also potentially harmful. AI devices raise a host of privacy concerns and interfere with the face-to-face interactions and self-driven play that children need to thrive.”

FreeTime on Alexa includes content targeted at children, like kids’ books and Alexa skills from Disney, Nickelodeon, and National Geographic. It also features parental controls, such as song filtering, bedtime limits, disabled voice purchasing, and positive reinforcement for using the word “please.”

Despite such controls, the child health experts warning against Echo Dot Kids wrote, “Ultimately, though, the device is designed to make kids dependent on Alexa for information and entertainment. Amazon even encourages kids to tell the device ‘Alexa, I’m bored,’ to which Alexa will respond with branded games and content.”

In Amazon’s April press release announcing Echo Dot Kids, the company quoted one representative from a nonprofit group focused on children that supported the product, Stephen Balkam, founder and CEO of the Family Online Safety Institute. Balkam referenced a report from his institute, which found that the majority of parents were comfortable with their child using a smart speaker. Although it was not noted in the press release, Amazon is a member of FOSI and has an executive on the board.

In a statement to WIRED, Amazon said, „We believe one of the core benefits of FreeTime and FreeTime Unlimited is that the services provide parents the tools they need to help manage the interactions between their child and Alexa as they see fit.“ Amazon said parents can review and listen to their children’s voice recordings in the Alexa app, review FreeTime Unlimited activity via the Parent Dashboard, set bedtime limits or pause the device whenever they’d like.

Balkam said his institute disclosed Amazon’s funding of its research on its website and the cover of its report. Amazon did not initiate the study. Balkam said the institute annually proposes a research project, and reaches out to its members, a group that also includes Facebook, Google, and Microsoft, who pay an annual stipend of $30,000. “Amazon stepped up and we worked with them. They gave us editorial control and we obviously gave them recognition for the financial support,” he said.

Balkam says Echo Dot Kids addresses concerns from parents about excessive screen time. “It’s screen-less, it’s very interactive, it’s kid friendly,” he said, pointing out Alexa skills that encourage kids to go outside.

In its review of the product, BuzzFeed wrote, “Unless your parents purge it, your Alexa will hold on to every bit of data you have ever given it, all the way back to the first things you shouted at it as a 2-year-old.”

Sources:
https://www.wired.com/story/congress-privacy-groups-question-amazons-echo-dot-for-kids/

two giants of AI team up to prevent the robot apocalypse

Kommentar verfassen

THERE’S NOTHING NEW about worrying that superintelligent machines may endanger humanity, but the idea has lately become hard to avoid.

A spurt of progress in artificial intelligence as well as comments by figures such as Bill Gates—who declared himself “in the camp that is concerned about superintelligence”—have given new traction to nightmare scenarios featuring supersmart software. Now two leading centers in the current AI boom are trying to bring discussion about the dangers of smart machines down to Earth. Google’s DeepMind, the unit behind the company’s artificial Go champion, and OpenAI, the nonprofit lab funded in part by Tesla’s Elon Musk, have teamed up to make practical progress on a problem they argue has attracted too many headlines and too few practical ideas: How do you make smart software that doesn’t go rogue?

“If you’re worried about bad things happening, the best thing we can do is study the relatively mundane things that go wrong in AI systems today,” says Dario Amodei, a curly-haired researcher on OpenAI’s small team working on AI safety. „That seems less scary and a lot saner than kind of saying, ‘You know, there’s this problem that we might have in 50 years.’” OpenAI and DeepMind contributed to a position paper last summer calling for more concrete workon near-term safety challenges in AI.

A new paper from the two organizations on a machine learning system that uses pointers from humans to learn a new task, rather than figuring out its own—potentially unpredictable—approach, follows through on that. Amodei says the project shows it’s possible to do practical work right now on making machine learning systems less able to produce nasty surprises. (The project could be seen as Musk’s money going roughly where his mouth has already been; in a 2014 appearance at MIT, he described work on AI as “summoning the demon.”)

None of DeepMind’s researchers were available to comment, but spokesperson Jonathan Fildes wrote in an email that the company hopes the continuing collaboration will inspire others to work on making machine learning less likely to misbehave. “In the area of AI safety, we need to establish best practices that are adopted across as many organizations as possible,” he wrote.

The first problem OpenAI and DeepMind took on is that software powered by so-called reinforcement learning doesn’t always do what its masters want it to do—and sometimes kind of cheats. The technique, which is hot in AI right now, has software figure out a task by experimenting with different actions and sticking with those that maximize a virtual reward or score, meted out by a piece of code that works like a mathematical motivator. It was instrumental to the victory of DeepMind’s AlphaGo over human champions at the board game Go, and is showing promise in making robots better at manipulating objects.

But crafting the mathematical motivator, or reward function, such that the system will do the right thing is not easy. For complex tasks with many steps, it’s mind-bogglingly difficult—imagine trying to mathematically define a scoring system for tidying up your bedroom—and even for seemingly simple ones results can be surprising. When OpenAI set a reinforcement learning agent to play boat racing game CoastRunners, for example, it surprised its creators by figuring out a way to score points by driving in circles rather than completing the course.

DeepMind and OpenAI’s solution is to have reinforcement learning software take feedback from human trainers instead, and use their input to define its virtual reward system. They hired contractors to give feedback to AI agents via an interface that repeatedly asks which of two short video clips of the AI agent at work is closest to the desired behavior.

This simple simulated robot, called a Hopper, learned to do a backflip after receiving 900 of those virtual thumbs-up verdicts from the AI trainers while it tried different movements. With thousands of bits of feedback, a version of the system learned to play Atari games such as Pong and got to be better than a human player at the driving game Enduro. Right now this approach requires too much human supervision to be very practical at eliciting complex tasks, but Amodei says results already hint at how this could be a powerful way to make AI systems more aligned with what humans want of them.

It took less than an hour of humans giving feedback to get Hopper to land that backflip, compared to the two hours it took an OpenAI researcher to craft a reward function that ultimately produced a much less elegant flip. “It looks super awkward and kind of twitchy,” says Amodei. “The backflip we trained from human feedback is better because what’s a good backflip is kind of an aesthetic human judgment.” You can see how complex tasks such as cleaning your home might also be easier to specify correctly with a dash of human feedback than with code alone.

 

Making AI systems that can soak up goals and motivations from humans has emerged as a major theme in the expanding project of making machines that are both safe and smart. For example, researchers affiliated with UC Berkeley’s Center for Human-Compatible AI are experimenting with getting robots such as autonomous cars or home assistants to take advice or physical guidance from people. “Objectives shouldn’t be a thing you just write down for a robot; they should actually come from people in a collaborative process,” says Anca Dragan, coleader of the center.

She hopes the idea can catch on in the industry beyond DeepMind and OpenAI’s explorations, and says companies already run into problems that might be prevented by infusing some human judgement into AI systems. In 2015, Google hurriedly tweaked its photo recognition service after it tagged photos of black people as gorillas.

Longer term, Amodei says, spending the next few years working on making existing, modestly smart machine learning systems more aligned with human goals could also lay the groundwork for our potential future face-off with superintelligence. “When, someday, we do face very powerful AI systems, we can really be experts in how to make them interact with humans,” he says. If it happens, perhaps the first superintelligent machine to open its electronic eyes will gaze at us with empathy.

Original Source from: https://www.wired.com/story/two-giants-of-ai-team-up-to-head-off-the-robot-apocalypse/

Google built its own computer chip specifically for running deep neural networks, called the Tensor Processing Unit, or TPU

Kommentar verfassen
Google server racks loaded with TPUsGOOGLE
GOOGLE OPERATES WHAT is surely the largest computer network on Earth, a system that comprises custom-built, warehouse-sized data centers spanning 15 locations in four continents. But about six years ago, as the company embraced a new form of voice recognition on Android phones, its engineers worried that this network wasn’t nearly big enough. If each of the world’s Android phones used the new Google voice search for just three minutes a day, these engineers realized, the company would need twice as many data centers.

At that time, Google was just beginning to drive its voice recognition services with deep neural networks, complex mathematical systems that can learn particular tasks by analyzing vast amounts of data. In recent years, this form of machine learning has rapidly reinvented not just voice recognition, but image recognition, machine translation, internet search, and more. In moving to this method, Google saw error rates drop a good 25 percent. But the shift required a lot of extra horsepower.

Rather than double its data center footprint, Google instead built its own computer chip specifically for running deep neural networks, called the Tensor Processing Unit, or TPU. “It makes sense to have a solution there that is much more energy efficient,” says Norm Jouppi, one of the more than 70 engineers who worked on the chip. In fact, the TPU outperforms standard processors by 30 to 80 times in the TOPS/Watt measure, a metric of efficiency.

GoogleChipInline.jpg

GOOGLE

A Neural Network Niche

Google first revealed this custom processor last May, but gave few details. Now, Jouppi and the rest of his team have a released a paper detailing the project, explaining how the chip operates and the particular problems it solves. Google uses the chip solely for executing neural networks, running them the moment when, say, someone barks a command into their Android phone. It’s not used to train the neural network beforehand. But as Jouppi explains, even that still saves the company quite a bit. It didn’t have to build, say, an extra 15 data centers.

The chip also represents a much larger shift in the world of computer processors. As Google, Facebook, Microsoft, and other internet giants build more and more services using deep neural networks, they’ve all needed specialized chips both for training and executing these AI models. Most companies train their models using GPUs, chips that were originally designed for rendering graphics for games and other highly visual applications but are also suited to the kind of math at the heart of neural networks. And some, including Microsoft and Baidu, the Chinese internet giant, use alternative chips when executing these models as well, much as Google does with the TPU.

The difference is that Google built its own chip from scratch. As a way of reducing the cost and improving the efficiency of its vast online empire, the company builds much of its own data center hardware, including servers and networking gear. Now, it has pushed this work all the way down to individual processors.

In the process, it has also shifted the larger market for chips. Since Google designs its own, for instance, it’s not buying other processors to accommodate the extra load from neural networks. Google going in-house even for specialized tasks has wide implications; like Facebook, Amazon, and Microsoft, it’s among the biggest chip buyers on Earth. Meanwhile the big chip makers—including, most notably, Intel—are building a new breed of processor in an effort to move the market back in their direction.

Focused But Versatile

Jouppi joined Google in late 2013 to work on what became the TPU, after serving as a hardware researcher at places like HP and DEC, a kind of breeding ground for many of Google’s top hardware designers. He says the company considered moving its neural networks onto FPGAs, the kind of programmable chip that Microsoft uses. That route wouldn’t have taken as long, and the adaptability of FPGAs means the company could reprogram the chips for other tasks as needed. But tests indicated that these chips wouldn’t provide the necessary speed boost. “There’s a lot overhead with programmable chips,” he explains. “Our analysis showed that an FPGA wouldn’t be any faster than a GPU.”

In the end, the team settled on an ASIC, a chip built from the ground up for a particular task. According to Jouppi, because Google designed the chip specifically for neural nets, it can run them 15 to 30 times faster than general purpose chips built with similar manufacturing techniques. That said, the chip is suited to any breed of neural network—at least as they exist today—including everything from the convolutional neural networks used in image recognition to the long-short-term-memory network used to recognize voice commands. “It’s not wired to one model,” he says.

Google has used the TPU for a good two years, applying it to everything from image recognition to machine translationto AlphaGo, the machine that cracked the ancient game of Go last spring. Not bad—especially considering all the data construction it helped avoid in the process.

https://www.wired.com/2017/04/building-ai-chip-saved-google-building-dozen-new-data-centers

What will the car of the future look like?

Kommentar verfassen
Technological breakthroughs such as autonomy are giving free rein on car design, so we’ve asked leading designers what the car of the future might look like

Autonomy, digitalisation, electrification and connected cars are no longer fashionable buzzwords looking to a brighter future.

Today, aspects of all three are already present on our roads, from cruise control functions that read the road ahead and adjust your speed, through to the self-driving Tesla Autopilot and Mercedes Driver Assist functions that are already on stream.

These are technological breakthroughs with far-reaching consequences; they are the result of the march of time and advances in understanding, and they are statesponsored because of the promise of fewer road injuries and accidents. They are an inevitability that will, in the words of Mercedes CEO Dieter Zetsche, prompt a profound change to cars “as radical as the industry has seen in its 120 years of existence”.

At the heart of this pivotal moment in time stands a generation of car designers with an entirely new rule book at their fingertips. But what does that rule book look like and how radically different is it?

Autocar polled leading designers from around the automotive industry to hear their views.

MICHAEL MAUER, Volkswagen Group head of design, on whether cars will end up looking the same:

“The mobility world of tomorrow gives us designers entirely new creative possibilities. Electric drives and autonomous driving remove any obstacles and change design more radically than has been the case in recent decades.

“But that does not mean we will have uniform autonomous vehicles. The streetscape of the future will become even more varied, even more colourful, even more emotional.”

SATORU TAI, executive design director for Nissan, on changing priorities and the short and longterm challenges:

“Cars may go through a phase of looking similar, but in the long run I think further advancement of technologies will then enable us to have more freedom in shaping unique designs, just as they did in the past.

“With the complete change of powertrains, the layout will become more flexible. We will no longer need an extended bonnet or bootlid. If we only pursue efficiency, I think the overall design of cars will become boxier and mono-volume orientated.

“Since many of the upcoming technologies are about man/machine interfaces, there will be a transition period and I am sure interior design will have more significance than exterior design. To a degree, the interior will influence the exterior design all the more and they will, eventually, resume the relationship they have today.”

GORDEN WAGENER, head of design at Mercedes-Benz, on bringing simplicity to complex solutions:

“Look at how much design has changed this company in the past three years. We’ve made the transition from an old luxury company to a modern luxury company, simply through design. Looking to the future with the challenges to come — digitisation, electrification — I think designers are the people to envision it.

“We’re living in the future; we’re five, 10, even 15 years into the future. Design has never been more important. There’s so much happening and, as designers, we’re really in the driver’s seat here. The new world will become very complex and it’s the designers who will try to make it simple.”

KLAUS BISCHOFF, Volkswagen design chief, on a focus on interiors:

“The biggest shift for design will be the interiors of EVs. Because we have pushed the ID concept’s climate control system into the nose, the dash can be pushed back 20cm — which gives a great deal more room in the cabin. Today’s car interiors are close to the driver, almost hemming them in; in future EVS, space in the cabin will be far greater.”

LAURENS VAN DEN ACKER, design chief for Renault, on whether to go radical or remain conventional:

“The first thing to say is that there’s never been a better time to be a designer. Technology means engineers can do things they couldn’t five years ago and that has opened up all sorts of avenues. Marketeers have realised that in a world of no really bad cars, design is what makes the difference.

“We can write our own future — and I don’t see car sharing taking that away. People will still care what their car looks like. People won’t want to be in a vehicle that looks like a trash can, and besides, most people won’t want to share a car. It’s something personal; it would be like sharing your cat.

“The biggest opportunity in the near future will be space; an electric drivetrain is 40% more compact than a combustion one, so that’s an opportunity. But how far do we go? I’m in favour of change but think customers will still want to see classic proportions. I don’t see a reason for revolution.”

SIMON HUMPHRIES, president of ED2, Toyota’s design HQ in Europe and one of the key development centres for Lexus and Toyota, on why there’s no single answer:

“Consumers’ values will become increasingly diverse, and consumers will become increasingly confident in their ability to choose without following mainstream trends. Acceptance of new, radical design and non-traditional hierarchies will result, and that may signal the end of mass trends in design as people seek new methods of self-expression.

“Size will no longer define the automotive hierarchy and branding strategies will have to change. The paradigm shift from gasoline to electric will not happen overnight; they will co-exist, resulting in each finding its own speciality. Choice will depend on lifestyle and the ‘allrounder’ car of today will be replaced by more specific designs, with the different experiences being offered becoming the brand differentiator.

“There will also be new influences from developing regions, leading to new concepts and ideas based on criteria other than the traditional European view of the car.”

MORAY CALLUM, vice-president of design at Ford, on how the designer’s job is changing:

“There’s more design to do because it’s more complicated. So much more goes into everything. When I started we chose between a 5.0in round headlight or a 7.0in headlight. Now we’ve got around 35 people on headlights, because there are around 50 different parts.

“We’re not just going to the car design schools to recruit now, because our role is getting wider as our relationship with the car is changing. As designers, we have an expanding role around how these systems we add work. For instance, the designer’s job is to make the [infotainment] logic logical to customers; we’ve got more interior designers than exterior designers now. You fall in love with the exterior but live with the interior — and most of the pain points are inside.”

ALFONSO ALBAISA, corporate vice-president and executive design director for Infiniti, on changing limits and how to persuade customers to embrace that change:

“I don’t feel there is a limit to designing cars for the future. The only issue is how we walk with our customer into the future, because the customer’s appetite for change is what we must relate to. Sometimes, depending on culture, the customer can be slightly conservative. This also depends on their social situation, but sometimes they are ambitious and expect significant design changes.

“I think premium customers are open to change if we provide a clear benefit to them. It’s important; if you change something significant, there must be very clear customer benefit. If there is not, the customer will reject it because they have so many good choices in the marketplace.

“In reality, the modern user experience and how it relates to and works with the owner has a much higher value than piping or wood on an interior, and I feel there is a great potential in the coming digital technologies.”

ROB MELVILLE, McLaren chief designer, on whether driver-focused supercars are less likely to change than conventional cars:

“They’ll change too — and soon. Our philosophy is to create breathtaking designs that tell the visual story of their function, and we have an amazing bandwidth of functionality and focus coming in our products. We plan to do this by using our advanced technologies, aerodynamic software and manufacturing processes to create our beautiful yet functional designs. We will continue to be brave and innovate.

“Clever design will be the dominant force and will always predominate over new legislation, which is an opportunity to find new solutions and make cars even more individual. It’s an exciting challenge for the team. The freeing up of crash structures will mean improved aerodynamics, which is fantastic, and the interior space/ volume of the car will be designed to suit our vehicle’s requirements.

“Customers will accept the changes as long as it is authentic, radical design. Radical design just to be trendy lacks integrity and this turns customers off. Our customers are very sophisticated and appreciate radical design that delivers improved experience, usability and fun. It has to put a smile on your face.”

STEFAN SIELAFF, Bentley director of design, on ultra-luxury design — and a history lesson:

“Maybe ‘transport boxes’ will be part of the future, but it will go one step at a time and I can say our customers want our cars because they make a statement, not just because they do a job.

“Bentley will always follow a fusion of performance and luxury; dynamics must be part of the mixture. But even if sometimes you will want to turn the seats around and leave the control to the systems, sometimes, at the right times, our customers will want to drive. It’s a compromise we know at Bentley; for 100 years our owners have done the same, albeit with chauffeurs driving.

“The question is not just about design but also technology. How will that change what we want from the interior space? And even if we give people more space, it won’t be about just opening the car up. Our customers want architecture, not just space.

“I am old enough to remember East and West Germany. In the East there was basically one car, a Trabant, available in five colours. The day the Berlin Wall came down, people were clamouring to change. That history lesson suggests there is no desire to own cars that look identical.”

http://www.autocar.co.uk/car-news/industry/what-will-car-future-look

Machines are becoming smarter marketers

Kommentar verfassen

artificial-intelligence-930x620

Marketing is only helpful when it’s meeting a need. It sounds simple, but those needs can be really tough to parse. Like any consumer, my needs evolve every day, if not every minute. I won’t stand for poorly targeted ads or messages that are irrelevant to me.

I work in marketing technology, and this industry has been talking about data-driven personalization for years. We’ve made a lot of progress, but we’re only just beginning to realize the potential of machine learning to match goods and services with a particular person in a specific situation.

Machines are changing how marketing is done. I’m not just talking about workflow automation or customer service bots. I’m talking about software that can help brands understand, meet, and even predict the subtlest of consumer needs.

It’s a new phase that I think of as Marketing 3.0. The 1.0 version, marketing in its early 20th century form, involved selling products to people who had demonstrated a need. The 1950s saw the rise of Marketing 2.0: ad men who shaped consumer desires to sell products. Machine learning allows marketers to move beyond this model and return to the original purpose of marketing, while adding speed and scale.

Marketing 1.0: Meeting needs as expressed
Marketing 2.0: Creating needs, then meeting them
Marketing 3.0: Machines analyzing needs, then meeting them

Marketing 3.0 uses machine learning to match product and consumer faster, more precisely, and in the right context; and to identify people who have an implied rather than overtly demonstrated need. Machines learn from a large pool of real-world examples, so they can predict future intent by observing past behavior. Marketers don’t have to comprehend the precise patterns that emerge from massive amounts of data or map out the rules that determine people’s behaviors.

In other words, machine learning shifts the role of the marketer from trying to manipulate customers’ needs to meeting the needs they actually have at a given moment.

Think about a BMW dealership looking to sell more of a particular model. They can use machine learning to identify indicators for people who bought a 5 Series in the past year: They researched similar cars like the Audi A6 and Mercedes E Class, they asked about mileage per gallon, and they had similar demographic traits.

Say I’m looking to buy a car and have a friend who recently bought a 5 Series. I’ve read about one of its new features: a 3D view of the car that I can see from my phone. When I search for “BMW 5 Series” on my iPhone, I’ll see a list of dealerships within a 10-mile radius of my regular commute. I call the dealership to ask about their inventory, and they know I’m ready to buy. I’m automatically matched with the sales rep who sold the same car to my friend, knows the specs I’m interested in, and can talk to me about 3D view.

I see massive opportunity to use predictive capabilities to link online and offline interactions — mobile ads, email campaigns, phone conversations, and in-person experiences. It’s becoming a reality as Google, Facebook, Apple, and Amazon continue investing in voice assistants and natural language processing technologies. Amazon is reportedly updating Alexa to be more emotionally intelligent. It’s not a huge leap to transition from making voice commands in my living room to calling a business and making a purchase directly through my Echo. A conversation is the most natural form of interaction, and the most conducive to forming relationships.

I think voice will be central to how marketers balance machine learning capabilities with the need to create human experiences. Even if machines can surface information and recommendations at exactly the right time, people still want human conversations, especially when it comes to buying complex or expensive products. I’m fine with Alexa ordering me a pizza, but not a car.

As I see it, the role of machines is to draw correlations between consumers’ behaviors and their ultimate intent. The role of the marketer is to figure out what can be automated (e.g., triggering an email after a purchase is made) and what can be augmented (e.g., predicting what products will most intrigue a customer) by using software. The next wave, Marketing 4.0, will take this a step further by meeting consumers’ expressed and unexpressed needs.

We’re moving toward a more predictive world in which machine learning powers the majority of interactions between consumers and brands. I don’t see this being at odds with human connection or authentic experiences. Marketing will be ambient and truly data-driven. It will catch up with consumer expectations and with the potential of technology to change how marketing is done

Machines are becoming smarter marketers

Samsung Plans To Give Galaxy S8 An AI Digital Assistant

Kommentar verfassen

All the cool companies have them: digital assistants. Apple has Siri, Microsoft has Cortana, and Google  has the cleverly named Google Assistant. Now, Samsung plans to bring its own iteration of a virtual assistant in the Galaxy S8 next spring, according to a new report from Reuters.

The assistant will be based on work by Viv Labs, a San Jose-based AI company that Samsung acquired this October (the move immediately fueled speculation that Samsung was moving into the AI space). The founders of Viv Labs already have a strong track record in the field as the creators of Siri, which Apple bought in 2010.

Samsung appears to be tapping into Viv’s existing strengths rather than aiming to revamp the platform. One of Viv’s hallmarks is that it is designed to be a one-stop-shop that works seamlessly with third-party services. “Developers can attach and upload services to our agent,” Samsung Executive Vice President Rhee In-jong said during a briefing, according to Reuters. “Even if Samsung doesn’t do anything on its own, the more services that get attached the smarter this agent will get, learn more new services and provide them to end-users with ease.”

If the digital assistant is a hit, it could help Samsung make up for its financial losses over the Galaxy Note 7 recall, which is projected to cost the company at least $5.4 billion. It could also rebuild consumer confidence after the Note 7 debacle and, more recently, a recall of a Samsung top-loading washing machine due to “impact injuries.”

But the company is entering a crowded market. Apple paved the way with Siri, though its early lead is shrinking after the launch of Google’s Assistant, which can tap into Google’s well-established knowledge graph and search capabilities. And there’s always Amazon Alexa, which already has a home in the smart-home devices the Echo, Dot and Tap.

„Every door can be unlocked.“  Ellen Fondiler

http://www.forbes.com/sites/shelbycarpenter/2016/11/06/samsung-plan-galaxy-s8-ai-digital-assistant

Artificial Intelligence Software Is Booming. But Why Now?

Kommentar verfassen

Marc Benioff, left, chief executive of Salesforce, talked with Matthew Panzarino, editor in chief of TechCrunch, at its Disrupt conference in San Francisco last week. Beck Diefenbach/Reuters

SAN FRANCISCO — This is the year artificial intelligence came into its own for mainstream businesses, at least as a marketing feature.

On Sunday, Salesforce.com, which sells online software for sales and marketing, announced it would be adding A.I. to its products. Its system, called Einstein, promises to provide insights into what sales leads to follow and what products to make next.

Salesforce chose this date to pre-empt Oracle, the world’s largest business software company, which on Sunday evening began its annual customer event in San Francisco. High on Oracle’s list of new features: real-time analysis of enormous amounts of data. Oracle calls its product Oracle A.I.

Elsewhere, General Electric is pushing its A.I. business, called Predix. IBM has ads featuring its Watson technology talking with Bob Dylan. These moves, along with similar projects at most major tech companies and consulting firms, represent years of work and billions in investment.

There are big pushes in A.I. in agriculture, manufacturing, aviation and pretty much every other sector of the economy.

It’s all very exciting, the way great possibilities are, and clearly full of great buzzwords and slogans. But will other companies see any value in all this or understand if A.I. has value for them?

“No one really knows where the value is,” said Marc Benioff, co-founder and chief executive of Salesforce. “I think I know — it’s in helping people do the things that people are good at, and turning more things over to machines.”

Mr. Benioff wasn’t selling Einstein’s capabilities short. He was talking about the long-term value of artificial intelligence, which is passing through a familiar phase — a technology that is strange and new, that sometimes overpromises what it can do and is headed for uses not easily seen at the start.

Cloaked inside terms like deep learning and machine intelligence, A.I. is essentially a series of advanced statistics-based exercises that review the past to indicate the likely future, or look at current customer choices to figure out where to put more or less energy.

Perhaps a better question than “What is the value?” of this explosion of advanced statistics is “Why now?” That shows both the opportunity and why many companies are scared about missing out.

Much of today’s A.I. boom goes back to 2006, when Amazon started selling cheap computing over the internet. Those measures built the public clouds of Amazon, Google, IBM and Microsoft, among others. That same year, Google and Yahoo released statistical methods for dealing with the unruly data of human behavior. In 2007, Apple released the first iPhone, a device that began a boom in unruly-data collection everywhere.

Suddenly, old A.I. experiments were relevant, and money and cheap data resources were available for building new algorithms. Ten years later, computing is cheaper than ever, companies live online and in their phone apps, and sensors are bringing even more unruly data from more places.

Amazon, Google and the rest have exceptional A.I. resources for sale, but many older companies are wary of turning their data over to these upstarts. That, along with fear of a competitor getting on top of A.I. first, is a big motivation for some to try things out.

Salesforce is selling Einstein as a system that can work predictive magic without having to look at your data, in what Mr. Benioff calls a “democratizing” move that will create millions of A.I. users who are not engineers.

He said this on his way to attend a series of customer focus groups around the country, however — strong evidence that customers don’t get it yet, even if they’re willing to try it.

“There’s fear of Google and Microsoft controlling everything, and there’s a desire to apply A.I. to anything that’s digital,” said Michael Biltz, managing director of Accenture’s technology vision practice. “People are going to have to experiment, most likely first on pain points like security and product marketing.”

How will we know when the A.I. revolution has taken hold? A technology truly matures when it disappears. We don’t marvel at houses with electricity now, or the idea of driving to work at 60 miles an hour. We can say “phone” and mean a hand-held computer with NASA-level processing power and a professional-quality camera for taking selfies with our drones.

A.I. is probably heading for the same places, invisibly sorting through lots of data everywhere to continuously update and automate most of our lives. Goodness knows what the weird new tech thing will be about at that point.

How Facebook Chatbots Can Revolutionize Your Social Media Strategy

Kommentar verfassen

How Facebook Chatbots Can Revolutionize Your Social Media Strategy

The artificial intelligence era… It’s all about embedding human smarts in machines.

Facebook chatbots are one application of this revolution, as they rapidly gain popularity and provide a new tool for marketers to leverage. These chatbots are the incorporation of automatic chatbots within Facebook Messenger.

Chatbots offer flexibility in order to automate tasks, and assist in retrieving data. They are becoming a vital way to enhance the consumer experience for the purpose of better customer service and growing interaction.

In April 2016, Mark Zuckerberg announced that third parties could use the messenger platform to create their own personal chatbot. Since then, the popularity of chatbots has rapidly grown all over the world.

In social media marketing chatbots have evolved, but their prime functionality remains the same, and that is to improve real-time engagement. Customers are always searching for prompt and ready replies to their comments and queries. The chatbots are designed in such a manner that they are able to answer most of the queries placed by customers, without human intervention. And this helps in bonding a strong relationship with your customers and potential crowds, without paying for high overheads on staff.

Two chatbots that have gained immense popularity in no-time are Apple’s Siri and Chotu Bot.

Chatbots: Why such a buzz at present?

Modern advancement in the field of artificial intelligence, which includes neural networking and deep learning, have permitted chatbots to acquire data sets exactly the way the human brain works. This is revolutionary.

Chatbots are instigating a stir in the present world of consumer services. Facebook created a revolution for technology by launching Facebook messenger chatbots which permit businesses to generate an interactive experience, content, e-commerce guides and automate customer service. Messenger has reached more than 900 million users, plus it offers the most striking platform to implement your desired bots.

Maybe the most renowned example  of a chatbot is Apple’s Siri. Like all chatbots, Siri is a perfect combination of pre-defined scripts and neural systems to anticipate a precise reaction to an offered conversation starter or explanation, permitting clients to skip steps while speaking. Siri is a masterpiece that took years for such a huge organization with loads of assets to develop.

Siri for Facebook chatbots

The second example is a chatbot from an organization that is not as famous as Apple. But still the chatbot is so efficient that it has been able to create a lot of buzz for itself in the market.

The Chotu Bot helps you replace various software and get detailed information on various topics such as wiki search, PNR status, Vehicle registration number etc. inside your messenger. And the developers behind the Chotu Bot are preparing to update it so that it can reply to most of the queries asked from all around the planet.

Chotu Bot for Facebook chatbots

How can a Facebook chatbot assist your marketing?

Facebook allows brands to connect their potential customers independently through these messenger bots which leads to a new era in advertising.

The basic idea behind launching the messenger bots is to connect all the people directly to the business in order to automate customer engagement and interactions. Now there are more than 11,500 bots that have been developed on messenger and nearly 23,500 developers signed up in order to build their own bots using tools offered by Facebook. This means it assists you to automate informal interactions between businesses and users.

Recently Facebook announced new features for bots which can easily respond with video, audio, GIFs, and such files that make you build your own bots with ease.

How do chatbots help e-commerce platforms?

Chatbots assist the e-commerce industry by providing functionality in areas such as security, management, monitoring, and customer engagement which are key elements of e-commerce businesses.

Self-service and automation are the ideal way to go ahead in e-commerce, this the ultimate reason why businesses are using chatbots.

Here are just some ideas for how chatbots can make customer engagement easier:

Convenient, contextual and in control

Facebook messenger for chatbots is focused on generating the greatest customer engagement experience. They offer automated updates about traffic, weather, automated messages and much more.

Easy setup and cost savings

Bots help you save time and reduce the cost of hiring staff.

Unprecedented customer reach

The new receive/send API allows you to connect with more than 950 million people in and around the world. That’s the reason bots are growing as the key tool for businesses to gain wonderful networking and commercial opportunities.

How can you use chatbots?

Chatbots make it possible to offer a more proactive, personal, and efficient consumer experience.

how to use chatbots for Facebook chatbots

  • Chotu, one of the leading chatbot technologies, is an AI robot on Facebook messenger that assists in accelerating customer information acquisition through Facebook messages. It provides all the needed information from your messages itself, rather than relying on several different apps working together. Chotu performs multiple tasks at a single time and offers 24×7 customer service.

chotu for customer experience for Facebook chatbots

  • Pizza Hut announced that Facebook messenger chatbots assist their customers in asking questions, viewing their current deals and much more. This helps Pizza Hut interact with their customers more easily at any time and from any place.

36 love questions for Facebook chatbots

  • 36LoveQuestions is a wonderful Facebook messenger chatbot that asks you 36 exact questions in order to determine whether you are in love with someone or not.

Chatbots: From the “simple” customer to enterprises

At present, chatbots are very prevalent in the customer space. From a business point of view, transportation businesses and e-commerce delivery enhance their chances by allowing their customer to purchase products more efficiently.

customer space for Facebook chatbots

But bots are rapidly moving across to the enterprise space as most companies are now building their own chatbots in order to generate better engagement with their customers and create additional value for their brand.

How chatbots are minimizing the gap between customers and brands

Public vs. Private

One of the major problems that various organizations had to face while promoting on social media was to provide a primary customer service to their potential clients. The best thing about [messaging bot] is that you can have a ‘Message Us’ option and truly use this as a one-on-one, private channel.

Consistency

Messaging is a continuous and real-time process between a customer and a brand. You can have a real-time chat with a specialist from the brand, then you can leave and return a day later and see the history… That is truly energizing since that begins to effect customer behavior.

Accessing an audience of over a billion people

Facebook commenced this entire chatbot furor in April by permitting outside bots on its messenger. So even in the worst case, this is the potential crowd you can reach.

Include different systems (WhatsApp has not joined the chatbot fleeting trend yet) and the aggregate gathering of people on messaging platforms is well in an abundance of 1.2 billion. This is the crowd that you can target directly and provide each one special attention.

Who does not like a personal exclusive service?

Chatbots are poised to reform the customer-brand interaction. Facebook knows the potential of personal messaging and they know this idea can be really useful for brands to retain their audiences for a longer period of time.

Any organization today with a chatbot has the capacity to gain customer insights. The more insights they gain, the better the brand messaging will become, which ultimately indicates better targeting and more sales. The best part is that these chatbots are relatively cheap compared to other applications.

A business which takes time to understand chatbots and execute them have a better chance of offering things to their customer and this will really help them build a nice strong relationship over time.

Chatbots are the perfect fit for the modern e-commerce company looking to ramp up customer service.

Companies which are using chatbots are likely to experience better results and acquire the ability to advertise and market new products which ultimately generate customer engagement.

http://www.jeffbullas.com/2016/09/02/facebook-chatbots-can-revolutionize-social-media-strategy

The new paradigm for human-bot communication

Kommentar verfassen

Editor’s note: Xuchen Yao is co-founder and CEO, and Guoguo Chen and Kenji Sagae are co-founders, of KITT.AI. Daniel Li is an associate at Madrona Venture Group.

Chatbots offer the promise of frictionless access to goods, services and information, but creating effective bots can be deceptively tricky.

The flip side of the opportunity to interact with users in a seamless, natural way is that user expectations can be prohibitively high. Bots need to be smart and provide greater convenience than apps — a very effective UI paradigm tailored for today’s mobile devices that has been carefully refined for more than a decade.

The good news is that the belief that bots must master human language or replace apps to succeed is false. Bots will engage with consumers in new ways that combine the strengths of humans and machines to allow both structured and unstructured information to be exchanged naturally and efficiently.

Communication velocity

One simple but intuitive way to measure the effectiveness of communication is to look at the amount of information exchanged per unit of time. Under this framework, text (e.g. SMS, chat, email) and speech (e.g. phone call) interactions differ in the amount of information that can be produced versus consumed.

image001

While we typically produce 120 to 140 words per minute when speaking, we can typically only write or type 40 to 70 words per minute. When we look at the speed of information consumption, reading speed in English is upwards of 200 words per minute, but listening speed is limited to the 120 to 140 words per minute of speech production.

SMS and chat apps have adapted to increase text production speed through autocorrect features and novel keyboards, but text production for humans will always be slower than consumption.

Imagine, however, a friend that can type, draw, look up information and find GIFs at superhuman speed, and produce buttons, menus and pictures to make your input faster. Better yet, your enhanced input is much easier for your friend to understand and does not take away the flexibility and familiarity of natural language when needed.

We may not be quite there yet, but we are very close, especially with well-constructed bots on certain platforms. Here is a look at the features of different bot platforms that are shaping human-bot communication toward a more efficient, robust and natural UI paradigm.

Quick-reply buttons

Quick-reply buttons are a simple and convenient way to save user time and prevent unexpected input. They are unique to human-bot communication as buttons are trivial for bots to create and easy for humans to use; benefits include enhanced communication speed and bot comprehension.

Facebook, Telegram and Kik bots all have quick-reply buttons, but under slightly different names, and some bots, such as the Sephora bot on Kik, use the quick-reply button as the primary mode of communication. Slack still lacks quick-reply buttons, but has message buttons with associated actions.

Telegram Custom Keyboard:

image002

Facebook Messenger Quick Replies:

image003

Kik Suggested Response Keyboard:

image004

Callback buttons

Callback buttons are similar to quick-reply buttons but allow for a broader range of potential interactions. When a callback button is clicked, it generates an HTTP call to a registered webhook that triggers a predefined action. Callback buttons are a great way to provide feedback, and they also provide a deeper analytics opportunity for the bot backend.

Slack Message Buttons:

image005

Messenger Postback Button:

image006

Telegram Callback Buttons:

image007

Structured information sharing

Sharing information that can be easily parsed programmatically takes the exchange of structured information from clunky in a language-only paradigm to easy and unambiguous in a hybrid paradigm.

For instance, sharing a location like “3rd & Madison” is ambiguous and slow for humans and machines to parse, while shared GPS coordinates can be quickly displayed with a map service and understood by bots.

Telegram SendContact and SendLocation:

image008

Facebook Messenger Location Sharing:

image009

Bot mentions

Related Articles

Telegram beefs up its bot platform Facebook fights Messenger spam with Asimov’s three rules of bots Never leave Slack with message button bots for using Kayak, Trello, and more Kik users have exchanged over 1.8 billion messages with the platform’s 20,000 chatbots

Inline bots are a great way to quickly obtain, send and share information during chats, without the need to jump out of the current interface (to go to another chat) or the current app (to go to another app).

Instead of multiple taps and menus to perform a specific function, an @ mention at a bot allows for a one-line interaction. Allowing bots to share conversational context with one another also greatly increases the speed of interaction because users no longer need to re-input data for each communication.

Telegram Inline Bot:

image010

Slack Bot Mention:

image011

The following table summarizes the added language-touch functionality provided by four popular chat and bot platforms. These features represent the beginning of a hybrid communication paradigm that will enable more efficient and effective communication with bots:

  • Quick-reply buttons: save user time and improve machine comprehension
  • Callback buttons: provide calls to action and back-end analytics
  • Structured info sharing: easily shares machine-readable information
  • Bot mention: make bots always present and easily accessible

image012

If your bot does not use a language-touch hybrid communication pattern, there are several other ways you can still take some of the UI mechanics from buttons and callbacks to build a better bot:

  • Build your system starting with humans in the loop to identify the most common communication patterns and exceptions to that pattern
  • Optimize dialogue for two-channel — fast and slow — communication with clear, well-defined responses (e.g., “Reply YES to buy”) or open-ended messages (“Can you tell me when the new Taylor Swift record comes out?”)
  • Use callback functions, even without native integration. For more complicated tasks, take users out of chat and move them to a point-and-click or touch interface that is better suited to the task at hand
  • Consider moving to a platform that is better optimized for new human-machine interaction

AI and NLP have a long way to go before bots achieve human-level communication. However, before that happens, new methods of human-machine communication will leverage the strengths of humans and machines to create new interaction paradigms that are as natural as our own language.

The new paradigm for human-bot communication

Battle of the assistants

Kommentar verfassen
Https%3a%2f%2fblueprint-api-production.s3.amazonaws.com%2fuploads%2fcard%2fimage%2f121380%2fcortanavssirionmacosthumb
Image: patrick lux/Getty Images

Despite what science-fiction wisdom says, talking to your computer is not normal. Sitting in the middle of a modern, open floor-plan office and saying „Hello, Computer,“ will garner some head-turns and a few scowls.

No matter. Companies like Microsoft, Amazon and Apple are convinced we want to talk to everything, including our desktop and laptop computers. Side-eye looks be damned.

Which brings us to today. Almost a year since Microsoft brought Cortana to Windows 10, Apple is following suit with Siri for the newly rechristened macOS.

Windows 10 with Cortana is, obviously, a shipping product, while macOS with Siri integration is in early beta. Even so, I can’t look at Siri’s first desktop jaunt in a vacuum, so when Apple supplied me with a MacBook running the beta of macOS Sierra (due to come to consumers in the fall), I compared the two desktop-based voice assistants. As you might surmise, they’re quite similar, but they have significant and strategic differences.

Where did they come from?

Siri arrives on the desktop as the oldest of the growing class of digital assistants, appearing first on the iPhone 4S in 2011. It’s long been rumored that it would eventually come to the Mac, so no one was surprised when Apple announced exactly that earlier this month at its Worldwide Developers Conference.

Cortana (which was named for the synthetic intelligence in Microsoft’s popular Halo game series), arrived with Windows 10 in 2015, a year after the digital assistant’s formal introduction on Windows Phone at the 2014 Microsoft Build conference.


Siri macOS

Siri lives in two spots on the desktop and asks you to let the system know your location.

Image: Apple

Like Cortana, Siri has a permanent place on the macOS desktop. Actually, it has two. A tiny icon in the upper right corner and then another in the macOS dock. Both launch the familiar Siri „waiting to help you“ wave.

On Windows, Cortana sits next to the Start Button. it has a circular halo icon and, next to that, the ever-present „Ask me anything.“


cortana

A click on the Cortana logo opens this Cortana window.

Image: microsoft

It’s at this point that the two assistants diverge. Cortana is a voice assistant, but, by default, it’s a text-driven one. Most people who use it will type something into the Cortana box. If you want to speak to Cortana — as I did many times for this article — you have to click the little microphone icon icon on the right side of the Cortana box.

While Cortana combines universal search with the digital assistant, Apple’s Siri drawn a line between the two.

Importantly, you can put Cortana in an always-listening mode, so it (she?) will wake when you say „Hey Cortana.“ Even though you can also wake the mobile Siri with „Hey Siri,“ macOS offers no such always-listening feature. For the purposes of this comparison, I left „Hey Cortana“ off.

Siri is a voice assistant. It has no text box. A click on either Siri icon opens the same black box in the upper right-hand side of the macOS desktop (it actually slides in from offscreen — a nice touch). As soon as you hit that button, Siri is listening, waiting for you to ask a question.

Sitting right next to Siri is Spotlight, which last year got a significant update. It’s a universal search that can pore over you Mac, the Web, iTunes, the App Store, maps.

So while Microsoft’s Cortana combines universal search with the digital assistant, Apple’s drawn a line between the two — sort of. Spotlight can perform many of the same searches as Siri. However, if you type a question into Spotlight, it may launch Siri. A trigger word appears to be „What’s.“

I really don’t know why Apple chose to keep Spotlight and Siri separate, but they may reconsider in future versions of macOS.

Battle of the assistants

It’s early days for Siri on the desktop, but I’m already impressed with its performance and intelligence — especially as it compares to Microsoft’s Cortana.

To test the two voice assistants, I first closed my office door. I wanted to speak in a normal voice and didn’t want to attract any annoyed stares.

Both Siri on macOS and Cortana start by asking you to open up your privacy settings a bit. They simply do their jobs better if they know where you are. So I followed Siri’s instructions and turned on location services on the macOS.


Here’s something else Siri on macOS and Cortana have in common: Both can tap into your system to, for example, find files and make system-level adjustments, but they’re both pretty inconsistent. Siri on macOS, obviously, is still a work in progress, so take these criticisms with a grain of salt. Even so, I suspect that there will, at least for some time, be limits to what Siri can do even after the forma macOS launch, especially as long as Spotlight survives.

When I asked Siri to „increase my screen brightness,“ it opened a System Preferences: Brightness slider box within Siri and told me „I made the screen a little brighter.“

Impressive.

When I asked Cortana the same question, it opened a Bing search result inside the Cortana box, which told me how to adjust screen brightness, but didn’t do it for me.

On the other hand, when I told Cortana to turn off my Wi-Fi, it turned it off, it returned a message of „Wi-Fi is now off“ and showed the setting to confirm.


Cortana and Siri Wi-Fi

On the left is how Cortana handles voice commands for turning on and off Wi-Fi. On the right is how Siri does it. When you turn off Wi-Fi (networking), you basically disable Siri.

Image: APPLE/MICROSOFT/COMPOSITE/MASHABLE

Siri can turn off Wi-Fi, too, but doing so also renders Siri for macOS useless. Unlike Cortana, it needs an Internet connection to work, which means once Siri on macOS has turned it off, you can’t use it to turn Wi-Fi back on. Even if you turn off network connectivity, Cortana will still be able to search your system.

Siri and Cortana excel at natural-language queries (asking questions in sentences), but Siri comes across as the smarter system.

It’s easy to check your schedule through both systems — you just need to ask one of them about your next appointment. However, Siri goes a big step further.


Siri on macOS

Changing you schedule should be this easy everywhere.

Image: apple

When I asked it about my next appointment, it showed me one for Thursday at 11:00 a.m. I then clicked the microphone icon below the calendar result and asked Siri, „Can you move that to 11:10.“ Siri responded, „Okay, I’ll make that change to your event. Shall I reschedule it?“ It then offered the option of confirming the change or cancelling it with my voice. Siri on macOS actually maintains the context between queries — that feels more like the future.

When I asked Cortana to make a similar change, it sent me to a Bing search result. (By the way, both voice assistants use Bing and neither will let you change it to Google.)

The level of conversational prowess in Siri could be a real game-changer and certainly puts Microsoft on notice.


macOS Siri

These are questions I can’t just ask Cortana.

Image: apple/composite/, mashable

Cortana and Siri on macOS both boast system access, but Siri does a better job of keeping track of system specs. I can ask about the speed of my system and how much iCloud storage I have left in Siri. Cortana, unfortunately, has no clue about my OneDrive storage and when I asked „How fast is my PC?“ I only got a Bing search result.

Where’s my stuff and who are you

Siri and Cortana each do a good job of finding system files that contain a keyword. For both, I asked, „Find me files with [keyword],“ and they both quickly showed me local, relevant results. Siri, however, excels at making results persistent. You can pin whatever you find to the notification center.


Cortana and Siri on macOS

On the left you can see that Cortana does a good job with image search, but won’t let me drag and drop from the window. On the right, Siri on macOS found me puppy pics and let me drag and drop one into an email that I plan to send to you.

Image: apple/microsoft/composite/mashable

Similarly, both voice assistants do a good job of finding images, but only Siri on macOS lets me drag and drop one of the image results into a document or email. When I tried to do the same thing with a Cortana result, it only dragged and dropped the HTML for the original query.

Siri did struggle with contacts. I tried initiating a text and got stuck in a sort of infinite loop — it just kept going back to asking me which of my duplicate contacts I wanted to text. This felt like a pre-release bug.

No winners yet

Since Apple is still working Siri for macOS, it’s way too soon to crown a voice-assistant champion. Even so, Siri on mac OS is already faster (Cortana’s voice recognition seems plodding by comparison) and it’s already outstripping Cortana on the intelligence front. On the other hand, Cortana truly shines when you can type into it, a feat impossible in Siri for macOS, unless you start in Spotlight and use one of the magic words to auto-launch Siri.

Microsoft, of course, has its own big Cortana update in the wings as part of the Windows 10 Anniversary Update due later this summer. It will increase Cortana’s intelligence and utility (order plane tickets, shop), but based on what I’ve seen in Siri for macOS, it may only help Cortana achieve parity on some features, while still leaving it trailing in others.

mashable.com/2016/06/22/siri-macos-vs-cortana