#basically algorithm is a bit more advanced then our modern day ai | Explore Tumblr posts and blogs

waqasblog2 · 6 years ago

Text

Difference Between Machine Learning, Artificial Intelligence, And Bots

Artificial intelligence (AI), once a topic only explored in science fiction movies, TV shows, and books is something that has quickly become a part of the world of today. In 1969, management consulting firm McKinsey & Company released an article claiming that computers were not smart enough to make any decisions, but rather the human’s intelligence behind the devices were the ones powering them. With modern computers replacing skilled human labor in such fields as medicine, agriculture, and education, it is fascinating to see how incorrect this claim turned out to be. Some may even argue that artificial intelligence is the way of the future. With buzzwords like “artificial intelligence,” “machine learning,” and “bots” tossed around, sometimes incorrectly interchangeably, it can be confusing to keep up with what is going on in this booming industry.

The easiest way to understand what artificial intelligence is would be to visualize three different-sized boxes: within the largest AI box is the machine learning box, and within the machine learning box is the deep learning box.

What is Artificial Intelligence?

Generally speaking, there are two different kinds of artificial intelligence: “general AI” and “narrow AI.” When work first started in the field of AI, researchers were determined to create complex machines that had the same characteristics of human intelligence. These machines were meant to have all of our senses and reason exactly how people do. This is the scope of general AI. Examples of general AI include Star Wars’ C3PO or The Terminator. Despite all of the technological advances that have been made, we are not yet at the point of developing such computer-run machines.

Narrow AI, on the other hand, is something we currently have the technology to achieve. Technologies that use this form of artificial intelligence display some degree of human intelligence and can perform some specific tasks very well. A great example of narrow AI in action probably exists in one’s pocket: Siri, Google Now, and Amazon Alexa. These personal assistants on smartphones help us find useful information when people ask using their voices, presenting information from one’s phone or sending information to other apps. To get a better understanding of where this intelligence is coming from, one must look inside the next box: machine learning.

What is Machine Learning?

Similar to how humans use knowledge and past experiences to approach new situations and challenges, “machine learning” is the technical discipline that relates with the use of algorithms to analyze and learn from data, and use the learning to perform future operations. Rather than having to hard-code specific software routines with actions to handle a particular problem, the computer is, in a sense, “trained” and has a set of data and algorithms that help it approach certain tasks. An example of machine learning is Google DeepMind’s AlphaGo program that learned how to play the board game “Go” and went on to defeat South Korean Master Lee Se-dol.

Telling Alexa to order more eggs from Amazon or texting a number to order food and have it delivered without ever talking to another human being is something that is not uncommon today. This is the basic idea behind bots – a bot is an application that performs an automated task. As explained above, these helpful tools are examples of narrow AI, as they possess some degree of human intelligence to carry out tasks.

Artificial intelligence is no longer something people can dream about one day coming into contact with. Although the technology to create human-like robots like people have seen on the big screens does not yet exist, companies like Apple, Google, and Amazon have been implementing computer-based tools that individuals can use to carry out tasks without the need of another human being. These tools have become increasingly more sophisticated and help make people’s lives a little bit easier, but it will be interesting to witness how these developments will impact society over time.

This article was authored by Nirmal Suthar, who is a Lead Software Engineer at Zymr.

This content was originally published here.

0 notes

androidskit-blog · 6 years ago

Text

Sony Xperia 1 with camera review

Sony Xperia 1 with camera review, People buy flagship phones as much for the cameras as for any other feature. Nearly every premium device from the likes of Huawei, LG, and Samsung has stepped up the game to include not one or two, but three cameras. The standard configuration for a modern flagship is now a high-quality primary lens, an optical telephoto lens, and a wide-angle lens. This applies to the Sony Xperia 1, which the electronics company is��just now getting to market. If you’re interested in learning everything there is to know about the Sony Xperia 1, check out our full review here. The purpose of this article is to dive deep into the camera situation and assess whether or not Sony can keep up.

Camera app

Sony’s camera app is powerful but perplexing. It contains the vast majority of advanced features flagship phone buyers expect, yet there are some glaring omissions. Let’s start with the dedicated camera button. Yes, the Xperia 1 has a physical shutter key, located where you expect to find it on the top right corner (when holding the phone sideways.) The button is a subtle two-stage key. Press it very lightly and the camera will focus on the subject. Press it all the way to fire off a shot. The difference between pressing the button gently and all the way is minimal. You can easily just smash the key down all the way when you intend to focus first. Alternately, you can do what we’re all using to do at this point: touch the screen where you want the camera to focus and then tap the software shutter button. Sony’s AI Cam is enabled by default. You can only ditch it by shifting to manual mode. What I find most frustrating is the lack of control over HDR. HDR functions automatically in AI Cam mode, which is to say you never know whether it is being used or not. The only way to take direct control over HDR is in manual mode. From my perspective, HDR should always be an easy-to-find feature. A basic on-screen toggle lets you switch between photo and video modes, while a series of controls line the opposite edge for functions such as aspect ratio, bokeh, flash, timer, and settings. Some of these could be easier to grasp. The bokeh tool, for example, is represented by one circle placed behind another. What the what? How does that equate “bokeh”?

On the whole, the camera app could be simplified quite a lot.

Jumping from one lens to another should be easier. The camera always launches with the standard/primary camera lens active. A small circle with a “1x” appears on the far right side. Tap it once to switch to the telephoto lens. The small circle then displays a “2x” inside. Tap once more to get to the wide-angle “w” lens. If you press the “1x” for a second, a slider bar appears for zooming between 1x and 2x, and on through to 10x (digitally). No matter what, you have to press the “w” to get to the wide-angle camera, and it pauses for a second before switching. It’s a confusing and inconsistent system. LG’s camera app is much simpler to decipher in this respect. A small button under the shutter button lets you access the advanced modes. These include portrait selfie, Google Lens, slow motion, AR effect, manual, creative effect, and panorama. Pick one, and then a little symbol pops up in the corner to tell you which you’re using. There’s no time-lapse mode, nor is there a dedicated portrait mode or even a night mode, which is both frustrating and puzzling. On the whole, the camera could be simplified and improved quite a lot.

Daylight

Any and every camera should excel at daylight shooting when the most light is available. It’s therefore amazing how poorly some perform. The Xperia 1 is all over the place in daylight situations. All four of these samples have bright and dark regions that aren’t particularly well balanced. What we notice most is the loss of detail in the darker spots, such as the trees in the first image, the sides of the buildings in the second and third images, and the pillars in the fourth. I’m glad the sky isn’t blown out in any of the images. These are passable shots, but not fantastic ones. Focus is mostly sharp, and colors are mostly accurate if a bit muted. For example, the yellow and red shades in the second image were brighter in real life. There isn’t too much noise, nor are compression artifacts obviously visible. These are passable shots, but not fantastic ones.

Color

Obtaining good color relies on a mix of things, including proper exposure and white balance. If one or the other is off, colors suffer. Some phone makers, such as Samsung, make up for this by boosting colors in the end results. Sony does not. Here we see the Xperia 1 at its best. The top two images turned out spectacularly well with rich, bright, accurate colors. There is no banding, and the transitions between shades are smooth. These images look exactly like what I remember seeing on the streets of New York City. Color me impressed (pun intended). You can see all the color, it’s just not as impressive as the real thing. The bottom two images are the Sony Xperia 1 camera at its most average. Both appear muted in terms of color tones and exposure. The fourth image is particularly frustrating because the tile mural was well lit and I was standing only a few feet away. You can see all the color, it’s just not as impressive as the real thing. It’s the inconsistency here that I don’t care for.

Detail

Preserving details relies on focus, resolution, and maintaining control over compression and noise. Once again we’re faced with inconsistency from the Sony Xperia 1 camera. In the top two images, the detail is clear enough that you can read the text in the images, there’s no doubt of that. Too bad neither is properly exposed. The images with the brushes in the foreground is terrible. Much of the detail in the leaves is lost on close inspection, with the green foliage blending together. It was much easier to tell the individual plants apart in person. The third image also has lots of noise in the sky. In the last picture, all the parts of the electric meters stand out and you can even tell where the gauges are pointed on the closer units. Here everything comes together, the exposure is on point, and there’s no noise at all.

Landscape

When shooting land- or cityscapes, focus and balance are generally what you seek. Three of these images provide those, one does not. What I like about image 1 is that the green looks rich, the sky is still blue, you can read the text on the sign, and even the darker areas have some detail. Image 2 shows sharp lines, accurate colors, and relatively good detail. Both these images are a bit on the noisy side, with compression artifacts here and there. Image 3 is a disaster. The phone’s HDR tool completely failed here. The sky is overblown and yet nearly all the detail on the statue is lost because it is underexposed. At least the foliage is green. The last image turned out fairly well. Despite the strong shadow, there’s lots of texture visible on the right wall compared to the fully sunlit left wall. You can see all the bricks and the sky is blue. There is still far too much noise.

Portrait

Fancy, effects-laden portrait shooting is all the rage these days. Many of today’s flagships include modes specifically for taking artful shots of our friends and family. In order to take portrait images such as these, you need to use the Xperia 1’s bokeh shooting tool. It’s not called “Portrait Mode” and there aren’t advanced tools such as studio lighting — another shortcoming of the camera app when compared to Samsung, Huawei, and others. The phone did do a decent job of outlining my profile cleanly and blurring out the background. I like that you can select the amount of background blur. In images 1 and 3, however, I look like I was artificially added to the pictures via PhotoShop. The second and fourth images look more natural. Exposure in all these shots is good, and I don’t see too much noise. I’m flummoxed that there’s no actual portrait mode, which might make capturing these a touch easier.

HDR

HDR shots generally blend several exposures to create a balanced whole, with detail visible in both bright and dark regions. The Xperia 1 struggles with HDR across the board. Images 1 and 4 are total failures of HDR. In the first, all the detail in the trees behind the fountain is lost due to underexposure. In the fourth, the top half of the image should have been bright with daylight and is instead dark and dreary. What is going on here, Sony?

It's evident that Sony's HDR algorithms need more tuning.

The second and third images are more balanced. They are each noisy, but at least the light and dark regions are better preserved. The second shot is particularly challenging because it has natural and artificial light mixed in a dark indoor environment. Some detail is lost on the second level, but this exposure is still fairly accurate. In the third pic, I appreciate that the blue sky is visible in the windows at all and that there’s some shading to the wooden roof far above the staircase. In all, however, it’s evident that Sony’s HDR algorithms need more tuning.

Low light

One of the biggest omissions of the Sony Xperia 1 camera is any sort of night mode. Sure, the AI Cam senses low light situations and takes steps to mitigate the exposure, but there isn’t a dedicated mode for shooting in the near dark. That’s a serious boo-boo considering phones such as the Huawei P30 Pro can practically see in pitch black night. All four of these images were taken post-sunset. The first, just after sunset, has a reasonable amount of detail in the trees, but the sky is overblown. The colors are about right. The second image actually turned out pretty well, and was true to the scene. Shame about the noise. The third image may be accurate, but is soft. The last image is clearly a stinker. For this, the camera took several seconds to capture the shot and we can still barely see what’s going on. The subject stands out, but the darker portions of the background are completely gone. Without an explicit low-light or night mode, the Xperia 1 trails the competition. The Google Pixel 3a XL, which costs half as much, delivers far superior results.

Selfie

All the Xperia 1’s portrait powers are found under the purview of the selfie camera. You can add effects, dial-in skin correction, make your eyes larger or your face thinner, and adjust the lighting. I captured these samples under a variety of conditions, including bright sunlight, indoors, and at nighttime. The results speak for themselves. The first two images, which were aided by sunlight, turned out well. The focus is good, colors are accurate, and things look pretty much as they did when the photos were taken. Things are a bit different in the third and fourth images. You can see that my face is a bit softer in the third image and the brick wall behind me looks a bit washed out. The last picture is a mess. Though it wasn’t that dark out, the Xperia 1 used the screen flash to light me up. While my face is properly exposed, the background is almost lost completely. Moreover, my face looks incredibly soft. On a whole, I’d call these average selfie shots at best.

Video

Flagship phones need to be able to capture 4K video, full stop. While we’d prefer to see 60fps, we can deal with 30fps which is where the Xperia 1 camera tops out. I captured a variety of video with the Xperia 1 in 4K and Full HD (the latter in 60fps). It may be hard for your eyes to really see the difference between the two, but the 4K footage from Sony impressed. I was pleased with the way the phone captured motion smoothly, despite the fact that I was moving around. Moreover, the phone’s sensors are better able to adapt to changes in lighting when recording video. Here, the Xperia 1 matches the competition.

Conclusion

As I said in my full Sony Xperia 1 review, I’m stunned at how poorly the Xperia 1’s camera performs. Not only is it not up to snuff when compared to other flagships, such as the Samsung Galaxy S10, Huawei P30 Pro, and Google Pixel 3 XL, it doesn’t even compare to the budget Google Pixel 3a XL. It’s hard to recommend a $949 phone when a $479 phone beats the snot out of it in the core category of photography. The bottom line, if you thought Sony’s adoption of the triple-camera setup would lead to a dramatic improvement in imaging quality, I’m here to tell you that’s not the case. Read the full article

#SonyXperia1Review

0 notes

ajayuikey · 5 years ago

Text

For the last two weeks, I’ve been flying around the world in a preview of Microsoft’s new Flight Simulator. Without a doubt, it’s the most beautiful flight simulator yet, and it’ll make you want to fly low and slow over your favorite cities because — if you pick the right one — every street and house will be there in more detail than you’ve ever seen in a game. Weather effects, day and night cycles, plane models — it all looks amazing. You can’t start it up and not fawn over the graphics.

But the new Flight Simulator is also still very much a work in progress, too, even just a few weeks before the scheduled launch date on August 18. It’s officially still in beta, so there’s still time to fix at least some of the issues I list below. Because Microsoft and Asobo Studios, which was responsible for the development of the simulator, are using Microsoft’s AI tech in Azure to automatically generate much of the scenery based on Microsoft’s Bing Maps data, you’ll find a lot of weirdness in the world. There are taxiway lights in the middle of runways, giant hangars and crew buses at small private fields, cars randomly driving across airports, giant trees growing everywhere (while palms often look like giant sticks), bridges that are either under water or big blocks of black over a river — and there are a lot of sunken boats, too.

When the system works well, it’s absolutely amazing. Cities like Barcelona, Berlin, San Francisco, Seattle, New York and others that are rendered using Microsoft’s photogrammetry method look great — including and maybe especially at night.

Image Credits: Microsoft

The rendering engine on my i7-9700K with an Nvidia 2070 Super graphics card never let the frame rate drop under 30 frames per second (which is perfectly fine for a flight simulator) and usually hovered well over 40, all with the graphics setting pushed up to the maximum and with a 2K resolution.

When things don’t work, though, the effect is stark because it’s so obvious. Some cities, like Las Vegas, look like they suffered some kind of catastrophe, as if the city was abandoned and nature took over (which in the case of the Vegas Strip doesn’t sound like such a bad thing, to be honest).

Image Credits: TechCrunch

Thankfully, all of this is something that Microsoft and Asobo can fix. They’ll just need to adjust their algorithms, and because a lot of the data is streamed, the updates should be virtually automatic. The fact that they haven’t done so yet is a bit of a surprise.

Image Credits: TechCrunch

Chances are you’ll want to fly over your house the day you get Flight Simulator. If you live in the right city (and the right part of that city), you’ll likely be lucky and actually see your house with its individual texture. But for some cities, including London, for example, the game only shows standard textures, and while Microsoft does a good job at matching the outlines of buildings in cities where it doesn’t do photogrammetry, it’s odd that London or Amsterdam aren’t on that list (though London apparently features a couple of wind turbines in the city center now), while Münster, Germany is.

Once you get to altitude, all of those problems obviously go away (or at least you won’t see them). But given the graphics, you’ll want to spend a lot of time at 2,000 feet or below.

Image Credits: TechCrunch

What really struck me in playing the game in its current state is how those graphical inconsistencies set the standard for the rest of the experience. The team says its focus is 100% on making the simulator as realistic as possible, but then the virtual air traffic control often doesn’t use standard phraseology, for example, or fails to hand you off to the right departure control when you leave a major airport, for example. The airplane models look great and feel pretty close to real (at least for the ones I’ve flown myself), but some currently show the wrong airspeed, for example. Some planes use modern glass cockpits with the Garmin 1000 and G3X, but those still feel severely limited.

But let me be clear here. Despite all of this, even in its beta state, Flight Simulator is a technical marvel and it will only get better over time.

Image Credits: TechCrunch

Let’s walk through the user experience a bit. The install on PC (the Xbox version will come at some point in the future) is a process that downloads a good 90GB so that you can play offline as well. The install process asks you if you are OK with streaming data, too, and that can quickly add up. After reinstalling the game and doing a few flights for screenshots, the game had downloaded about 10GB already — it adds up quickly and is something you should be aware of if you’re on a metered connection.

Once past the long install, you’ll be greeted by a menu screen that lets you start a new flight, go for one of the landing challenges or other activities the team has set up (they are really proud of their Courchevel scenery) and go through the games’ flight training program.

Image Credits: Microsoft

That training section walks you through eight activities that will help you get the basics of flying a Cessna 152. Most take fewer than 10 minutes and you’ll get a bit of a de-brief after, but I’m not sure it’s enough to keep a novice from getting frustrated quickly (while more advanced players will just skip this section altogether anyway).

I mostly spent my time flying the small general aviation planes in the sim, but if you prefer a Boeing 747 or Airbus 320neo, you get that option, too, as well as some turboprops and business jets. I’ll spend some more time with those before the official launch. All of the planes are beautifully detailed inside and out and except for a few bugs, everything works as expected.

To actually start playing, you’ll head for the world map and choose where you want to start your flight. What’s nice here is that you can pick any spot on your map, not just airports. That makes it easy to start flying over a city, for example. As you zoom into the map, you can see airports and landmarks (where the landmarks are either real sights like Germany’s Neuschwanstein Castle or cities that have photogrammetry data). If a town doesn’t have photogrammetry data, it will not appear on the map.

As of now, the flight planning features are pretty basic. For visual flights, you can go direct or VOR to VOR, and that’s it. For IFR flights, you choose low or high-altitude airways. You can’t really adjust any of these, just accept what the simulator gives you. That’s not really how flight planning works (at the very least you would want to take the local weather into account), so it would be nice if you could customize your route a bit more. Microsoft partnered with NavBlue for airspace data, though the built-in maps don’t do much with this data and don’t even show you the vertical boundaries of the airspace you are in.

Image Credits: TechCrunch

It’s always hard to compare the plane models and how they react to the real thing. Best I can tell, at least the single-engine Cessnas that I’m familiar with mostly handle in the same way I would expect them to in reality. Rudder controls feel a bit overly sensitive by default, but that’s relatively easy to adjust. I only played with a HOTAS-style joystick and rudder setup. I wouldn’t recommend playing with a mouse and keyboard, but your mileage may vary.

Live traffic works well, but none of the general aviation traffic around my local airports seems to show up, even though Microsoft partner FlightAware shows it.

As for the real/AI traffic in general, the sim does a pretty good job managing that. In the beta, you won’t really see the liveries of any real airlines yet — at least for the most part — I spotted the occasional United plane in the latest builds. Given some of Microsoft’s own videos, more are coming soon. Except for the built-in models you can fly in the sim, Flight Simulator is still missing a library of other airplane models for AI traffic, though again, I would assume that’s in the works, too.

Image Credits: TechCrunch

We’re three weeks out from launch. I would expect the team to be able to fix many of these issues and we’ll revisit all of them for our final review. My frustration with the current state of the game is that it’s so often so close to perfect that when it falls short of that, it’s especially jarring because it yanks you out of the experience.

Don’t get me wrong, though, flying in FS2020 is already a great experience. Even when there’s no photogrammetry, cities and villages look great once you get over 3,000 feet or so. The weather and cloud simulation — in real time — beats any add-on for today’s flight simulators. Airports still need work, but having cars drive around and flaggers walking around planes that are pushing back help make the world feel more alive. Wind affects the waves on lakes and oceans (and windsocks on airports). This is truly a next-generation flight simulator.

Image Credits: Microsoft

Microsoft and Asobo have to walk a fine line between making Flight Simulator the sim that hardcore fans want and an accessible game that brings in new players. I’ve played every version of Flight Simulator since the 90s, so getting started took exactly zero time. My sense is that new players simply looking for a good time may feel a bit lost at first, despite Microsoft adding landing challenges and other more gamified elements to the sim. In a press briefing, the Asobo team regularly stressed that it aimed for realism over anything else — and I’m perfectly ok with that. We’ll have to see if that translates to being a fun experience for casual players, too.

[ad_2] Source link

Microsoft’s new Flight Simulator is a beautiful work in progress – TechCrunch For the last two weeks, I’ve been flying around the world in a preview of Microsoft’s…

#beautiful #flight #Microsofts #progress #Simulator #TechCrunch #work

0 notes

dizzedcom · 5 years ago

Text

Image Credits: Microsoft

Image Credits: TechCrunch

Once you get to altitude, all of those problems obviously go away (or at least you won’t see them). But given the graphics, you’ll want to spend a lot of time at 2,000 feet or below.

Image Credits: TechCrunch

But let me be clear here. Despite all of this, even in its beta state, Flight Simulator is a technical marvel and it will only get better over time.

Image Credits: TechCrunch

Image Credits: Microsoft

Image Credits: TechCrunch

Live traffic works well, but none of the general aviation traffic around my local airports seems to show up, even though Microsoft partner FlightAware shows it.

Image Credits: TechCrunch

Image Credits: Microsoft

Microsoft’s new Flight Simulator is a beautiful work in progress For the last two weeks, I’ve been flying around the world in a preview of Microsoft’s…

0 notes

ladystylestores · 5 years ago

Text

How EQT Ventures’ Motherbrain uses AI to find promising startups

Since Sweden’s EQT Ventures embraced AI to drive the way it makes investments, the company has learned that reaping the benefits of algorithms is a journey full of detours that involve experimenting, fine-tuning, and adaption to achieve the promised efficiencies and insights.

Following the firm’s launch in 2016, a team there developed Motherbrain, an AI-driven system whose goal is to help EQT spot the hidden gems that no one else sees and back them early. So far, Motherbrain has directly led to investments in five startups out of the 50 the firm has made.

That may seem like a disappointment. But according to Henrik Landgren, the EQT partner who took the lead on developing the system, the practical value so far has been the ability to make partners more productive by prioritizing which companies are worth spending time getting to know.

“Leveraging data has been one of our core pillars,” Landgren said. “We wanted to create a different fund.”

The wins sound less sexy, but Motherbrain has increased the number of companies the firm can sort through. The development of Motherbrain is a good lesson in the reality of what AI can deliver, and how achieving those productivity gains involves adjusting expectations and being patient.

AI for VC

EQT Ventures is the venture arm of Swedish company EQT Partners, a global private equity firm. In May 2016, EQT created a venture firm that started life with a $600 million fund. It then closed a second fund in 2019 for $760 million. The funds target tech companies in Europe and the U.S. and invest anywhere from $3.5 million to $85 million in a round.

From the start, the venture firm knew it wanted to take a data-driven approach to investing, even if it wasn’t exactly clear what that meant at that time. So EQT brought in Landgren, who had been Spotify’s vice president of analytics.

“No one knew what it would look like, but we could all agree on the vision that in 5 to 10 years, the best investor out there will be extremely data-driven,” he said. “And a lot of the work we do today in our investments will have to be automated.”

Mixing AI and investing is an idea that’s attracted a growing number of firms. InReach Ventures has developed its own AI investment platform. And San Francisco’s SignalFire has been tracking billions of data points across millions of startups globally since 2013. But on the whole, personal networking and word-of-mouth remain the dominant tools of the trade.

“The industry of venture investing is really old and quite conservative and traditional,” Landgren said. “And we saw that there are opportunities in the modern cloud-based approach.”

Each of these firms had to build its AI capability from the ground up. For EQT, that meant addressing the obvious need for strong data sources, as well as the less obvious need to rethink the way the firm operated daily to fully leverage Motherbrain.

On the data side, Landgren acknowledges that the first two years involved a lot of iterating. EQT tapped into third-party databases, and it created a process for scraping data and images from startups’ websites. It now draws on about 40 sources of data.

Meanwhile, Landgren began developing the algorithms, which use convolutional neural networks, or CNNs. Those algorithms now map connections between data such as app store downloads, information on previous investors, website traffic, founders’ resumes, and more.

But initially, there was a fair bit of trial and error as the firm experimented with different mixes of data and writing the algorithms. That meant learning which questions are the best ones to ask about a company, and what criteria were likely to reliably lead to the best prioritization.

Also, developing Motherbrain meant rethinking basic tasks related to the firm’s daily workflow. EQT’s 30-person team initially relied on third party messaging, planning, and email apps. The team had a range of cloud-based services that were disconnected, leaving internal data scattered among different silos. Eventually, Landgren realized Motherbrain was missing out on this rich source of data and began re-architecting all communications and data so it passed through the AI platform.

“Probably even more important has been changing the process of how we work with the platform and our day-to-day work,” he said. “And that has proven to be extremely important because we have found now that we have been able to use the AI to build much better predictive models.”

Just as critical has been creating an internal discipline so that partners follow the recommendations generated by Motherbrain, in small ways and large ways. If the firm isn’t making decisions about which companies to talk to based on those AI insights, there’s no way to test whether they are useful or improving. Those decisions, in turn, are fed back into the platform to create another way for it to learn.

“To train those models using AI, you have to have a process where people do what the machine tells you to do,” Landgren said.

So far, the most useful function has been eliminating the least promising startups. That frees up partners to more effectively use their limited time to do the human-driven work of meeting founders, analyzing products, and other forms of due diligence.

But Motherbrain also offers practical help with such tasks as preparing for meetings by generating notifications that include summaries of past interactions with a person. The system is also constantly scanning outside sources to find news items related to companies of interest or competitors.

The Big AI Prize

For all those smaller steps, the firm has started to see some of the bigger payoffs it wanted Motherbrain to deliver. Of the roughly 50 investments EQT had made since 2016, five are in companies that were directly identified by its AI platform:

In most of these investments, EQT took a proactive approach by contacting the startups first before receiving any type of pitch. They made these advances with a high degree of confidence and knowledge before any discussions began. That allowed EQT to get its foot in the door before the startups had been overwhelmed with offers.

“There is such a big difference if you talk to a company proactively and you reach out because we know something good, instead of them coming to us and we’re not sure exactly why they’re coming to us,” Landgren said. “There’s typically all this work that we have to go through to figure out the underlying reason for why they contacted us. Is it because their money is running out? Did they just overhaul everything?”

This handful of investments has Landgren optimistic that EQT’s development of Motherbrain is on the right track and that the firm will continue to see bigger payoffs from it.

“This is something that has been on our roadmap and in our vision for a long time,” Landgren said. “It’s just a matter of time and resources to get there.”

Source link

قالب وردپرس

from World Wide News https://ift.tt/39sQEGi

#World Wide News

0 notes

sualkmedeiors · 6 years ago

Text

How AI Will Redefine the Modern Marketing Campaign

Today, the term “marketing” encompasses a slew of interconnected tasks. Instead of simply promoting goods or services, marketers are spending colossal amounts of time researching, creating, managing, analyzing, and reporting on campaigns. Marketers are ever-present through all stages of the sales journey, and their jobs are never done.

It wasn’t always this way. While basic marketing has been around as long as commerce—with ancient traders displaying their wares as attractively as possible—our modern understanding of the field evolved alongside the Industrial Revolution. This era marked the start of mass production and a shift from making products to buying them. The glut of manufactured goods created a new need: courting potential customers and standing out in an increasingly competitive marketplace. Thus, modern marketing was born.

We’re experiencing another revolution today. Just as machines redefined marketing in the eighteenth century, today’s technology is advancing the way we exchange goods and services yet again. The biggest evolution will come from artificial intelligence (AI), which uses data and algorithms to model and predict behaviors. AI is already influencing the marketing profession, but this is just the beginning. Marketing campaigns of the future might look wildly different than today’s.

The Changing Face of Marketing

Years from now, we’ll still recognize the core elements of campaigns, even if the way we approach them changes. We’ll continue to consider the who, what, where, when, and how. And we’ll always measure what happened. But there will also be major shifts. AI has the sophistication to test infinite combinations of variables over time to optimize the sales experience for every person. Equipped with this technology, marketers will move from mass campaigns for large groups to hyper-individualized campaigns built from a huge supply of plug-and-play microcontent.

Let’s explore how AI will redefine the elements of marketing, and look at what a campaign of the future may look like.

Audience

In marketing’s humble beginnings, an audience was simply whoever happened to walk by. Tradespeople were limited by location, and sophisticated outreach was simply nonexistent. Today’s marketers have come a long way by using rules-based segmentation and Smart Lists. But with the widespread adoption of AI, these methods will eventually seem as outdated as word of mouth.

As AI helps pinpoint preferences, trends, and past purchasing behavior, the “who” of each campaign will become increasingly important, and marketers will create individualized tracks for every customer. They will also leverage this massive amount of data to do a cluster analysis and identify natural affinities within groups.

But it won’t stop there. AI will also analyze data to continually expand and create lookalike audiences for colossal—yet incredibly targeted—growth. In the future, how marketers select audiences will change. Instead of them telling their CRM and automation software whom to include in campaigns, the technology will tell marketers whom to target.

Content

Everyone knows that one size doesn’t fit all. But most marketers still take a wide-spray approach—like producing content for the entire population of Facebook or Twitter—rather than a precise one. This will change. Soon, marketers will use AI and machine learning to create hyper-personalized messaging. The technology will help companies identify preferences and values so they can create microcontent and assemble it in real time based on an individual’s specific needs and interests.

Instead of a 100-page “definitive guide” to a topic, marketers will produce hundreds of small, varied bits of content. Then, they’ll assemble the pieces like a puzzle. The result? A completely unique picture for each individual that’s driven by data to be as rich and relevant as possible. In the future, marketers will move from distributing messages to the masses to assembling bits of content and creating completely personalized campaigns based on real-time engagement.

Editorial Calendars

The more personalized campaigns become, the less effective a large-scale editorial calendar will be. Most B2B databases house thousands of contacts. If each of them is on a tailored journey within a unique campaign, how could you possibly manage all those calendars?

The answer is, you won’t. Technology will likely evolve to filter contacts into “categories” of journeys or campaigns. AI will organize groups of people into similar categories, and marketers will be able to analyze the different rates of engagement among them. In the future, marketers will move from an editorial strategy and struggling to visualize campaign operations in calendars to a more intelligent strategy where calendars have gone extinct.

Creative

AI already has an impressive creative portfolio. In the last few years, advancements have allowed machines to compose music, paint pictures, play board games, and even generate human-like faces with stunning accuracy. So, where does that leave the actual people?

We often think of creativity as a uniquely human trait. And even with AI continually evolving, it will remain that way. Though AI can innovate, it must first be programmed by humans. Even “creative” AI can only master one skill, while humans can be great at singing and playing chess.

One of AI’s most attractive traits is its ability to can take on mundane campaign tasks, like crunching performance numbers, so humans can focus on more strategic and creative tasks. In the future, human marketers will give up burdensome, time-consuming numerical tasks to AI, freeing up time for people to do what they’re great at: being creative.

Measurement

In addition to transforming marketing journeys themselves, AI will also change the way we analyze our investments. With powerful technology running campaigns, marketers will focus less on engagement metrics because AI will already be optimizing them. Instead, people can take a more holistic approach to the numbers.

Marketers are already becoming comfortable with this concept. Take trade shows, for example. If an organization participates but only closes one deal, the attendance costs may just barely be covered. But the value of participation cannot be measured by only that one sale. ROI also comes from increased awareness and brand affinity. In other words, it’s a longer-term play.

Marketing will always have to prove its worth and show ROI, but marketers might be doing so in ways they can’t presently imagine. In the future, marketers will move from struggling to show true ROI not even trying to show ROI—at least, not in the ways we currently do.

Unlocking the Power of AI

These days, inboxes are flooded with irrelevant marketing emails. Campaigns adopt a mass approach, sending messages to millions of people with the hope that a fraction of 1% open it. This relegates companies to marketing purgatory: the spam folder. But AI, with its tremendous computing power and vast amount of accessible data, can do amazing things—and it will transform marketing forever. As marketers, we are living in an incredible time. Technology is evolving so quickly that we’re witnessing our profession change right before our eyes. The future of marketing is on the horizon. It’s up to us to seize it.

Ready to learn more? Check out our Practical Guide to Artificial Intelligence for Marketers for ideas about how to get started.

The post How AI Will Redefine the Modern Marketing Campaign appeared first on Marketo Marketing Blog - Best Practices and Thought Leadership.

from https://blog.marketo.com/2019/02/how-ai-will-redefine-the-modern-marketing-campaign.html

0 notes

darkammarketing · 6 years ago

Text

How AI Will Redefine the Modern Marketing Campaign

The Changing Face of Marketing

Let’s explore how AI will redefine the elements of marketing, and look at what a campaign of the future may look like.

Audience

Content

Editorial Calendars

Creative

Measurement

Unlocking the Power of AI

Ready to learn more? Check out our Practical Guide to Artificial Intelligence for Marketers for ideas about how to get started.

The post How AI Will Redefine the Modern Marketing Campaign appeared first on Marketo Marketing Blog - Best Practices and Thought Leadership.

from http://bit.ly/2MLJAt5 via IFTTT via Blogger http://bit.ly/2D05nc0

0 notes

racheltgibsau · 6 years ago

Text

How AI Will Redefine the Modern Marketing Campaign

The Changing Face of Marketing

Let’s explore how AI will redefine the elements of marketing, and look at what a campaign of the future may look like.

Audience

Content

Editorial Calendars

Creative

Measurement

Unlocking the Power of AI

Ready to learn more? Check out our Practical Guide to Artificial Intelligence for Marketers for ideas about how to get started.

The post How AI Will Redefine the Modern Marketing Campaign appeared first on Marketo Marketing Blog - Best Practices and Thought Leadership.

from RSSMix.com Mix ID 8217493 https://blog.marketo.com/2019/02/how-ai-will-redefine-the-modern-marketing-campaign.html

0 notes

maxslogic25 · 6 years ago

Text

How AI Will Redefine the Modern Marketing Campaign

The Changing Face of Marketing

Let’s explore how AI will redefine the elements of marketing, and look at what a campaign of the future may look like.

Audience

Content

Editorial Calendars

Creative

Measurement

Unlocking the Power of AI

Ready to learn more? Check out our Practical Guide to Artificial Intelligence for Marketers for ideas about how to get started.

The post How AI Will Redefine the Modern Marketing Campaign appeared first on Marketo Marketing Blog - Best Practices and Thought Leadership.

from RSSMix.com Mix ID 8217493 https://blog.marketo.com/2019/02/how-ai-will-redefine-the-modern-marketing-campaign.html

0 notes

zacdhaenkeau · 6 years ago

Text

How AI Will Redefine the Modern Marketing Campaign

The Changing Face of Marketing

Let’s explore how AI will redefine the elements of marketing, and look at what a campaign of the future may look like.

Audience

Content

Editorial Calendars

Creative

Measurement

Unlocking the Power of AI

Ready to learn more? Check out our Practical Guide to Artificial Intelligence for Marketers for ideas about how to get started.

The post How AI Will Redefine the Modern Marketing Campaign appeared first on Marketo Marketing Blog - Best Practices and Thought Leadership.

from RSSMix.com Mix ID 8217493 https://blog.marketo.com/2019/02/how-ai-will-redefine-the-modern-marketing-campaign.html

0 notes

archiebwoollard · 6 years ago

Text

How AI Will Redefine the Modern Marketing Campaign

The Changing Face of Marketing

Let’s explore how AI will redefine the elements of marketing, and look at what a campaign of the future may look like.

Audience

Content

Editorial Calendars

Creative

Measurement

Unlocking the Power of AI

Ready to learn more? Check out our Practical Guide to Artificial Intelligence for Marketers for ideas about how to get started.

The post How AI Will Redefine the Modern Marketing Campaign appeared first on Marketo Marketing Blog - Best Practices and Thought Leadership.

from RSSMix.com Mix ID 8217493 https://blog.marketo.com/2019/02/how-ai-will-redefine-the-modern-marketing-campaign.html

0 notes

supriyogupta · 8 years ago

Text

Quest For Fighters: Get Ahead Of The Impending Artificial Intelligence Apartheid

Before I begin, let it be said that much of what follows here is a distillation of writings in various formats and virtually every sentence that follows can be Googled back to a source from where it has been extracted, usually verbatim in order to avoid hashing up through reinterpretation.

India debates fiercely today about fighter aircrafts it should buy. The filters we are using date back to the 1950s when strategic alliances were defined by the aircraft that each superpower was willing to ‘share’ with countries within their sphere of influence. India had played the Non-Aligned card to little avail and was mostly stuck with Russian aircraft along with a handful of British and French aircraft. As we embrace the free market of fighter aircraft sales, we are heading towards a moment in history when a different veil is coming down to separate the powers – one that will rocket the leaders away from the rest at a speed and pace that will define the next 200 years of power balance.

This is really about the technologies that have resulted in a harsh battle developing at the crossroad of technology, business and human development. Words like artificial intelligence, deep learning, neural networks are finding their way into mainstream lexicons. They represent a whole new civilizational process coming into play. The other day, a young Indian entrepreneur with a large online commerce platform, solemnly laid out the spectre of what he calls the “end of humanity” – when machines through self-learning have learnt to design, further develop and replicate themselves, eliminating jobs, wresting control and becoming autonomous in their behaviour.

Hang in there. Sounds a lot like a geek on weed? Ok. Here is something to chew on: In 1988, the U.S.S. Vincennes mistakenly destroyed an Iranian airbus due to an autonomous friend/foe radar system. The missing piece in 1988 was cognition and discrimination – understanding data correctly and then exercising discrimination at the point of engagement on the basis of superior processing capability. Artificial Intelligence has been around in defence – going as far back as the early part of the 20th Century – but its only recently with the ramping up of processing speed and the creation of neural networks that the ballgame is changing.

Even now, defence technology is fairly primitive. Russian Kalashnikov arms manufacturer has developed a fully automated combat module based on artificial neural networks which allows it to identify targets, learn and make decisions on its own. Kalashnikov promises to unveil a whole line of neural network based products. Primitive but already a bit scary that machines will decide on targets and take autonomous decisions. But Artificial Intelligence is going way ahead.

Let’s just take a quick look at a news that bypassed much of mainstream media but is definitely a direct pointer to how artificial intelligence is taking the art of warfare – actually, the art of warfare on century’s old game boards – to a different level, thereby setting the stage for real advancements on battlefields. And the real fun part is that the leader in this play is none other than Google. (As also, Amazon, Facebook and a host of others who are trying to get your free market choices narrowed down to a behavioural construct)

Here is a short update from Singularity Hub: “The AlphaGo AI that grabbed headlines last year after beating a master of the board game Go has just been trounced 100-0 by an updated version. And unlike its predecessor, the new system taught itself from first principles paving the way for AI that can think for itself.

When chess fell to AI in the 1990s, computer scientists looking for a new challenge turned to the millennia-old Chinese game Go, which despite its simpler rules has many more possible moves and often requires players to rely on instinct.

It was predicted it would be decades before an AI could beat a human master, but last year a program called AlphaGo developed by Google’s DeepMind subsidiary beat 18-time world champion Lee Sedol 4–1 in a series of matches in South Korea.

It was a watershed moment for AI research that showcased the power of the “reinforcement learning” approach championed by DeepMind. Not only did the system win, it also played some surprising yet highly effective moves that went against centuries of accumulated wisdom about how the game works.

Now, just a year later, DeepMind has unveiled a new version of the program called AlphaGo Zero in a paper in Nature that outperforms the version that beat Sedol on every metric. In just three days and 4.9 million training games, it reached the same level that took its predecessor several months and 30 million training games to achieve. It also did this on just four of Google’s tensor processing units—specialized chips for training neural networks—compared to 48 for AlphaGo.”

To understand where we are headed, we need to have some basic understanding of what these mean – at least today. Here is a simple explanation from nvidia:

“The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both. Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it… Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task….Another algorithmic approach from the early machine-learning crowd, Artificial Neural Networks, came and mostly went over the decades. Neural Networks are inspired by our understanding of the biology of our brains – all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.

You might, for example, take an image, chop it up into a bunch of tiles that are inputted into the first layer of the neural network. In the first layer individual neurons, then passes the data to a second layer. The second layer of neurons does its task, and so on, until the final layer and the final output is produced.

Each neuron assigns a weighting to its input — how correct or incorrect it is relative to the task being performed. The final output is then determined by the total of those weightings. So think of our stop sign example. Attributes of a stop sign image are chopped up and “examined” by the neurons — its octogonal shape, its fire-engine red color, its distinctive letters, its traffic-sign size, and its motion or lack thereof. The neural network’s task is to conclude whether this is a stop sign or not. It comes up with a “probability vector,” really a highly educated guess, based on the weighting. In our example the system might be 86% confident the image is a stop sign, 7% confident it’s a speed limit sign, and 5% it’s a kite stuck in a tree ,and so on — and the network architecture then tells the neural network whether it is right or not… Today, image recognition by machines trained via deep learning in some scenarios is better than humans, and that ranges from cats to identifying indicators for cancer in blood and tumors in MRI scans. Google’s AlphaGo learned the game, and trained for its Go match — it tuned its neural network — by playing against itself over and over and over. Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon. AI is the present and the future. With Deep Learning’s help, AI may even get to that science fiction state we’ve so long imagined.”

Nonetheless, these are still primitive days in the AI, Neural Networks and Deep Learning space for defence which has typically led technology so long. The sector is desperately trying to learn from firms as diverse as Google to fashion and Facebook. Look around carefully and see how artificial intelligence, deep learning and neural networks are already changing your life. Facebook is slowly understanding you and feeding you with content you tend to agree with. That’s a software network that’s learning how you think and starts mimicking your behaviour. Extend such ‘learning’ to more complex situations. For instance, when faced with a mob with sticks, stone and also a few armed with guns, police forces find it difficult to distinguish under pressure and apply counter measures equally. What if a machine can separate these two as different threats and apply counter measures differently?

Now coming to the Gripen / Rafale / Eurofighter. Or any of the fighters currently under development as opposed to those that are getting souped up with a few impressive add ons which dont fundamentally change the performance or capabilities of the aircraft but make them look both modern and, of course, carry the tag of being experienced.

Here is what is not on the platter –though, admittedly, the news being put out today is at a fairly generic level: F-35s, F-22s and other fighter jets will soon use improved “artificial intelligence” to control nearby drone “wingmen” able to carry weapons, test enemy air defenses or perform intelligence, reconnaissance and surveillance missions in high risk areas.

Or how about: The U.S. Air Force, working with Lockheed Martin’s Skunk Works have demonstrated another round of flight capabilities for an autonomous F-16 fighter jet, which is meant to show what an eventual “Unmanned Combat Air Vehicle” (UCAV) could do using technology they’ve developed. During this demonstration, the experimental aircraft was able to “autonomously plan and execute air-to-ground strike missions” based on mission information provided, as well as the assets made available by the planning team, but it was also able to react to unexpected changes during the mission, including “capability failures, route deviations and loss of communication,” according to a Lockheed news release. The talk of fighter aircraft town when it comes to technology is “an F-35 computer system, Autonomic Logistics Information System, that involves early applications of artificial intelligence wherein computers make assessments, go through checklists, organize information and make some decisions by themselves – without needing human intervention.”

The problem here is that ‘unmanned aircraft’ or collaborative wingmen is still at low levels of artificial intelligence and more about data linking, some degree of machine and a lot of pre-programming with some reactive scenario adjusting software that deals with unplanned – but not unexpected situations – with planned – and not self-learning – solutions.

The real big leap will take place in the very near future as processing capacity reaches mindboggling levels. The difference is that most aircraft today in the air are less smart than a standard Smart Phone and way, way dumber than iPhone X or Samsung 8. However, the future will be less hardware driven as focussed on how much AI can be integrated into the existing hardware.

At the same time, the ethics of AI is likely to lead to a situation very similar to the nuclear divide – with some countries storming ahead and then cordoning off the rest due to the growing fears of random and indiscriminate decision making by what are essentially machines. As Techcrunch points out, “Use of autonomous weapons on the battlefield is obviously controversial, of course. The UN seems to be moving towards a possible uniform ban on AI-powered weapons, and it’s obviously the basis for more than one dystopian sci-fi story. Critics argue use of autonomous weapons could increase the number of civilian deaths in warfare, and muddy responsibility for the loss of those lives – proponents essentially argue the opposite, saying use of autonomous systems will decrease casualties overall and lead to shorter, more decisive conflict.”

The future of air combat will be almost nothing like what we see, plan and project today. At the hub of future air battles will be aircraft with awesome levels of situational awareness married to neural networks that play a bit of a chess game, processing data, selecting options and launching engagements at a speed about ten times faster than your current Facebook suggesting friends or topics to read when you show your preference for a particular engagement. Meaning, almost instantly. If that sounds flippant, think about all the data that is married between your phone, your gmail, your social media and your browsing habits as you move very very randomly between hundreds of thousands of bytes of data. Compared to that, the elements and variables in the air in a war scenario are fairly limited, easily identifiable and highly predictable in trajectory, engagement options, capacity and capability.

So how are the world powers going about it?

According to The Hague Centre for Strategic Studies, “While still somewhat lagging behind on its great power rivals in terms of deep machine learning capabilities, the Russian Federation has displayed a steady commitment to developing and deploying a wide range of robotic military platforms, including unmanned ground vehicles (UGVs), with the full backing of its MoD and domestic industries: in January 2017, President Putin called for the creation of “autonomous robotic complexes”.

Speaking in 2015, Robert Work, the then-US deputy secretary of defense, emphasized “human-machine collaboration combat teaming”, arguing that: “Early adoption will be a key competitive advantage, while those that lag in investment will see their competitiveness slip”. In this speech to the Defense One National Security Forum conference, Work identified five pillars to the military future:

1 Autonomous deep learning machine systems which are able to see the patterns through the chaff of hybrid warfare, to give early warning that something is happening in gray zone conflict areas (such as the Ukraine), and which are able to respond at extreme speed, and under rapidly shrinking engagement windows. Such learning systems might, he argues, fill the gap in those fields – such as air defense or cyber defense – where human operators alone cannot achieve sufficient speed to stop or degrade a determined attack.

2. Human machine collaboration, which will include the promotion of so-called ‘Centaur’ warfighting, going from the observation that teams combining the strategic analysis of a human with the tactical acuity of a computer, reliably defeat either human-only or computer-only teams across many games.

3. Assisted human operations, where wearable electronics, uploadable combat apps; heads up displays, exoskeletons, and other systems, can enable humans on the front line to perform better in combat.

4. Advanced human-machine combat teaming where a human working with unmanned systems is able to take better decisions and undertake cooperative operations. Examples of these are the Army’s Apache and Gray Eagle UAV systems, which are designed to operate in conjunction. Other examples are drone ‘motherships’; electronic warfare networks, or swarming systems which will help transform operations by enabling one mission commander to direct a full swarm of micro-UAVs.

5. Network-enabled semi-autonomous weapons, where systems are both linked, and hardened to survive cyberattack.

But as the Hague Centre rightly concludes, “Our own hunch is that AI (and a number of attendant technological developments that are co-emerging around big data) may have a much more disruptive impact on the essence ‘defense’ than the focus on AI-enhanced physical robotics and how they might affect our current way of safeguarding defense suggest.”

While AI and policy is a big discussion in itself, returning to the opening question, India’s fighter aircraft purchase programme needs to be focussed on acquiring a platform whose avionics is expandable. Even as the Americans are trying hard to get Indians to focus on things like thrust and vectoring and so forth, the problem is that there is little porting capability in most aircraft for future AI capability to be incorporated.

Second, AI developments have just about reached the tipping point and are going to scale up quickly, very quickly. Even as India struggles with ‘design and development’ of basic fighter aircraft frameworks, the world of air defence is rocketing away that may well make much of what we are investing our time and effort in quite irrelevant. The key is to find a partner which is willing to bring India in from the front door and give a seat on the table of AI development now. Clearly, the US simply does not see India as a partner in key technology domains. On the other hand, the Swedes, French or Germans just might need the tech hands Indians end up bringing to the table for accelerating much of their thinking on AI. Companies like Saab have put on their board AI specialists – a clear recognition that the company would need to be taking tough decisions on future investments in that space.

The biggest reason India needs to get on board with a partner country willing to share the primary work table on AI is simply this: Very soon as autonomous machine intelligence starts dominating the space, the fear and threat of such technology getting into the wrong hands will start the ball rolling for the next generation of tech apartheid. India was for long a nuclear pariah, a missile pariah and a super computer pariah. All at the behest of sound American policing of the world where India was not seen to be a trustworthy partner. This time will be no different.

There is another BIG reason that the tech entrepreneur with a very successful online platform pointed out while reviewing this piece and I quote him verbatim: “Because of the self-reinforcing and exponential nature of AI progress, the gap between number 1 and number 2 will keep on increasing (in terms of capabilities and not in terms of months) as we move forward. Infact by the time it reaches its pinnacle, even a 3-6 month gap would mean 100X more capability (as opposed to 10%higher capability today)

Because of the domain agnostic nature of AI algorithms, it is possible to achieve much progress in the understanding and development using some other domain and then apply those learnings in a totally different domain. E.g the same deep mind that beat GO champions lowering the energy usage in google data centres. Hence it’s important to choose the domain / problem statements that provide a fertile ground for the AI to evolve fast rather than choosing the problem that you want solved. If you closely observe google, that is what they are doing. The ultimate objective is not to create AI for Olympic Games, but almost 90% of the early effort has been on games (GO being just one of those).”

#IAF ArtificialIntelligence FighterAircraft DeepLearning Neuralnetworks singularity Gripen Eurofighter F-35

0 notes

sualkmedeiors · 6 years ago

Text

How AI Will Redefine the Modern Marketing Campaign

The Changing Face of Marketing

Let’s explore how AI will redefine the elements of marketing, and look at what a campaign of the future may look like.

Audience

Content

Editorial Calendars

Creative

Measurement

Unlocking the Power of AI

Ready to learn more? Check out our Practical Guide to Artificial Intelligence for Marketers for ideas about how to get started.

The post How AI Will Redefine the Modern Marketing Campaign appeared first on Marketo Marketing Blog - Best Practices and Thought Leadership.

from http://feedproxy.google.com/~r/modernb2bmarketing/~3/OEVhM3sh-l8/how-ai-will-redefine-the-modern-marketing-campaign.html

0 notes

sualkmedeiors · 6 years ago

Text

How AI Will Redefine the Modern Marketing Campaign

The Changing Face of Marketing

Let’s explore how AI will redefine the elements of marketing, and look at what a campaign of the future may look like.

Audience

Content

Editorial Calendars

Creative

Measurement

Unlocking the Power of AI

Ready to learn more? Check out our Practical Guide to Artificial Intelligence for Marketers for ideas about how to get started.

The post How AI Will Redefine the Modern Marketing Campaign appeared first on Marketo Marketing Blog - Best Practices and Thought Leadership.

from https://blog.marketo.com/2019/02/how-ai-will-redefine-the-modern-marketing-campaign.html

0 notes

darkammarketing · 6 years ago

Text

How AI Will Redefine the Modern Marketing Campaign

The Changing Face of Marketing

Let’s explore how AI will redefine the elements of marketing, and look at what a campaign of the future may look like.

Audience

Content

Editorial Calendars

Creative

Measurement

Unlocking the Power of AI

Ready to learn more? Check out our Practical Guide to Artificial Intelligence for Marketers for ideas about how to get started.

The post How AI Will Redefine the Modern Marketing Campaign appeared first on Marketo Marketing Blog - Best Practices and Thought Leadership.

from Marketo Marketing Blog http://bit.ly/2MLJAt5 via IFTTT

#IFTTT #Marketo Marketing Blog

0 notes