Category: Disruptive Technology

Disruptive innovation is an idea that goes against the norms, significantly changing the status quo of regular business growth and developing what is known as a new market that accelerates beyond the scope of existing markets.

At the beginning, it may not be as good as the technology it’s replacing, but it’s usually cheaper, meaning more people can snap it up and it can become a commodity fast.

The key differentiator between disruptive innovation and standard innovation is that what is considered to be a revolutionary innovation doesn’t shift the entire market. It may make a difference to some people, but if it’s not accessible to all or doesn’t have an effect on the entire market, it’s not considered disruptive.

Although the introduction of cars in the 19th century was a revolutionary innovation, not everyone was able to afford them and so they didn’t become a commodity until much later.

Horse-drawn carriages remained as the primary mode of transport for a long time after the introduction of motorised vehicles – in fact until the Ford Model T was launched. It was a mass-produced car that made vehicles more affordable for everyone and is therefore considered a disruptive innovation.

The history of disruptive innovation

The idea of a disruptive innovation was first introduced by Harvard Business School scholar Clayton M. Christensen in 1995. He wrote a paper called Disruptive Technologies: Catching the Wave and then discussed the theory further in his book The Innovator’s Dilemma.

This latter book examined the evolution of disk drives but formed the argument that its the business model that allows for a product to become a disruptive innovation rather than the technology itself.

The term is now considered ‘modern jargon’ by some thinkers and publications, although it’s still widely used.

Examples of disruptive innovation in technology

The iPhone Although products aren’t necessarily always the definition of disruptive innovation, the iPhone is one that had a huge impact on the industry. It redefined communication and although at launch had a pretty narrow focus, quickly became mainstream, mobilising the laptop and developing a new business model in the process – one that relied upon app developers (a new, revolutionary concept in itself) to make it a success.

The iPhone completely disrupted the technological status quo – as smartphones because a way to replace laptops and evolved into the iPad – even further eradicating the need for a laptop.

Video on demand The concept of streaming TV programmes at any time to your TV would have been completely insane probably a decade ago.

The ability to stream content using the internet direct to the TV has completely disrupted the market. According to a recent report by the Office of National Statistics, half of adults now consume content using streaming services.

But it’s also disrupted the advertising market, with an entirely new revenue stream for sponsors and advertisers, shifted the balance in rights and caused startups to overtake traditional TV networks in popularity and income.

Digital photography Digital photography is a solid example of disruptive innovation, because not only did it introduce a technology – and whole ecosystem of accessories that grew in popularity because of it (such as memory cards, photo printers etc.), but it also completely shifted the manufacturer market, knocking Kodak, one of the previous market leaders, into oblivion.

The concept of digital photography was actually developed by Kodak engineer Steve Sasson, but it wasn’t popularised by the firm because Kodak wanted to concentrate on film – its bread and butter. Unfortunately, other firms jumped onto the idea and it completely revolutionised photography, leaving film a hobbyist technology in many cases, rather than a commercial opportunity.

Source: ITPRO

Artificial intelligence is taking the world by storm, and many experts posit that the technology has brought us to the cusp of a fourth industrial revolution that will fundamentally alter the business landscape. AI and machine learning are responsible for a constant stream of innovation and disruption in the way organizations operate. To avoid being left behind, business leaders need to prepare for this future now.

While the earliest iterations of AI emerged in the 1950s, hardware limitations prevented the technology from reaching its true potential. Of course, the amount of processing power in our pockets today would have astounded scientists in that era, and advanced algorithms are allowing us to put it to work, combing through reams of data in seconds at the mere touch of a button.

AI isn’t exactly real intelligence, but it is capable of spotting patterns buried deep within data sets that human eyes may or may not notice, and in a fraction of the time. Additionally, due to deep learning techniques, it’s capable of learning and improving over time, meaning it becomes more and more effective at its job. Thanks to this functional facet, AI is powering an exciting array of applications from investment strategies to autonomous vehicles.

AI is sweeping through one industry after another, and while the technolgy is still in its infancy, it’s important to get started implementing now, so you don’t fall behind in the future. Rome wasn’t built in a day, so its safe to assume that implementation of AI technology won’t happen overnight either. To position your company in the sweet spot to grasp this incredible opportunity now, here are three steps to follow.

1. Cultivate an open culture.

According to McKinsey, a profit gap is already emerging between early AI adopters and those who have yet to implement the technology. Unfortunately for the firms that are being left behind, catching up is more than the matter of purchasing new software.

While the tempo of technological change is difficult enough to keep up with, the pace of cultural change is glacial. To take advantage of AI requires a team effort, which necessitates organizations build that culture of trust and openness to encourage collaboration. Encourage this kind of open culture now — for example, promote cross-team collaboration, invite process experimentation and redefine key performance initiatives — to foster positive attitudes toward technological change and AI adoption.

2. Partner with the pioneers.

AI is on the bleeding edge of technological innovation, and the pioneers pushing it to the next innovative level are startups. These small companies aren’t going it alone, however. From financial institutions to automotive companies, large organizations are funding incubators and accelerators to nurture the next generation of startups whose technology will change industries.

Working with startups has a couple advantages for bigger companies. According to Hossein Rahnama, founder and CEO of Flybits, “Joining up with a young company gives each entity a partner to lean on and grow with over the years.” He adds, “Through these partnerships, you’re not just procuring technology; you’re gaining access to talent, consulting services, new ideas, and more.” Working with small, agile startups provides an excellent launchpad for technology strategy and adoption.

3. Capitalize on creativity.

AI can crunch numbers better than anyone you could ever hope to hire, but it can’t do everything. It certainly can’t exercise creative problem-solving capabilities, and it’s still up to your employees to turn the insights AI unlocks into high-level strategies that drive business value.

The issue is that many companies see AI as the remover of jobs, when really it is a job creator and efficiency upper. Instead of requiring a creative team to constantly multitask between crunching the numbers and strategy, use AI to perform some of the grunt work, and free up your creative teams to do what they do best. The initial implementation might make operations less efficient, but over time, your marketing and other creative teams will become much better at wielding the technology. With a bit of experience, they’ll aim it at the right data to determine your company’s next step in reaching its goals.

As AI becomes more widespread, it will morph from a competitive advantage to the price of admission. To keep pace with the rest of the pack, start creating an implementation strategy today.

Source: Entrepreneur

Microsoft recently taught its XiaoIce chatbot, a Chinese language conversational AI, how to interpret pictures as poems. We’re not sure if that counts as inspired writing, but it’s an interesting step toward better mimicking humans.

It’s not the first AI to write poetry, but it’s the first we’ve seen that can generate Chinese language poems inspired by images. There may be a little bit lost in translation, but some of the bot’s bars aren’t too shabby:

Wings hold rocks and water lightly

in the loneliness

Stroll the empty

The land becomes soft

The problem of teaching AI how to generate text descriptions or captions for images is a popular one among machine learning researchers. If an AI can eventually be taught to see the world the same way we do, starting with one image at a time, eventually we can teach it to see things our way all the time.

As humans, we can look at a picture and make inferences, point out objects, describe people’s facial features, and more – this is all based on our ability to draw upon context. Computer vision and deep learning artificial neural networks make it possible for machines to do this as well, but so far they’re nowhere near as good at it as an average human child.

One of the paths to creating a better autonomous image description AI is to keep tweaking a machine learning model until its output is indistinguishable from the work done by humans. But, at some point, simple descriptions like “I see a red apple on a brown table” are good enough to fool everyone. It’s pointless to try and figure out if a machine or a human wrote that, because it could obviously be either.

To further their work researchers have to find ways to make machines better at mimicking us, which means trying more difficult natural language processing tasks. And poetry is obviously more complex than simple captions.

So how does it work? Scientists input some rules and then make a neural network split itself into a side that generates a poem, and a side that judges a poem. If the judge side thinks the rules have been met and particular poem is good enough for human eyes, it lets it through. A human then checks the results. If it’s not good enough, they go back to tweaking the parameters until its spitting out good stuff.

Microsoft has actually been working on this problem for awhile, having created an AI recently that generates English language poetry from images. But, writing Chinese poetry involves a different set of rules and parameters, even for humans. Chutian Xiao, a researcher studying English poetry at Durham University, says “it is a tricky thing to learn poetry and write poems in different languages.” and he’s just talking about humans.

When it comes to Chinese poetry, the rules have actually changed over the years. It’s harder, according to the Microsoft researchers working on the Chinese language bot, to write modern poetry:

While traditional Chinese poetry is constructed with strict rules and patterns (e.g., five-word quatrains are required to contain four sentences and each sentence has five Chinese characters, also words need rhymes in specific positions), modern Chinese poetry is unstructured in vernacular Chinese. Compared to traditional Chinese poetry, although the readability of vernacular Chinese makes modern Chinese poetry easier to strike a chord, errors in words or grammar can more easily be criticized by users. Good modern poetry also requires more imagination and creative uses of language. From these perspectives, it may be more difficult to generate a good modern poem than a classic poem.

So how good is Microsoft’s AI at writing poetry? Well, that’s entirely subjective. Futurism writer Dan Robitzki said its English language poet bot was as good as an angsty teenager. I, by contrast, rather liked some its stuff. But I’m more interested in LeCun than Longfellow, so I tend to lean in support of the machines.

To scientifically determine the efficacy of their poetry bot, the researchers conducted experiments where, according to them, people overwhelmingly chose the results from their bot over results from similar neural networks:

A large scale user study indicates that our generated moderna poems earn much more favor than the generated captions. The reasons are that our generated poems are more imaginative, touching and impressive.

While a rose by any other name would surely smell as sweetly – to paraphrase The Bard – whether or not a machine can actually create art, much less poetry, is still up for debate.

Source: TNW

Nearly a decade ago scientists got pretty excited over a glow coming out of the center of our galaxy. They believed it to be gamma ray emissions resulting from self-destructing dark matter. Unfortunately, it turns out, the Milky Way’s glowing “bulge” wasn’t related to suicidal dark matter. It was probably just gas.

A team of researchers from the University of Amsterdam and the University of Grenoble Alpes today published work indicating the glow is actually just a profile of the stars the bulge surrounds.

We find that an emission profile that traces stellar mass in the boxy and nuclear bulge provides the best description of the excess emission, providing strong circumstantial evidence that the excess is due to a stellar source population in the Galactic bulge. We find a luminosity to stellar mass ratio of (2.1 ± 0.2) × 1027 erg s−1 M−1 for the boxy bulge, and of (1.4±0.6)×1027 erg s−1 M−1 for the nuclear bulge. Stellar mass related templates are preferred over conventional DM profiles with high statistical significance.

The other explanation, the one we all wished were true, was that suicidal dark matter was sending gamma rays exploding out into the galaxy. Were this the case, we could finally zero in on dark matter and prove its existence.

Alas, while we won’t be unraveling dark matter’s mysteries in our own backyard – at least not by examining that particular galactic bulge – we’ve gained an amazing companion on our quest: AI.

The scientists didn’t just guess that the profile of the bulge’s glow better matched the gravitational profile of the stars near it than that of hypothetical dark matter. They used machine learning to do the heavy lifting and demonstrated through scientific rigor that it was the most likely explanation. To do so, they created a set of proprietary algorithms called Sky Factorization with Adaptive Constrained Templates (SkyFACT) to handle the huge problem of figuring out the exact specifications of a glowing field of gamma rays in space.

Another recent experiment involving gamma rays and AI could help scientists approach the issue from a different angle. Scientists working beneath the France/Switzerland border at the Large Hadron Collider (LHC ) accelerated actual atoms for the first time last month.

The LHC team sped up ionized lead atoms to near the speed of light, and observed the zany particle physics that ensued. This was the first time this had been done with an atom — previous attempts used protons and atomic nuclei. In successfully accelerating an actual atom (though stripped of all but one electron) the team believes they’ve laid the groundwork for what could eventually become an incredibly high-intensity gamma ray factory.

This, of course, means that researchers will be able to create Incredible Hulks on demand. Actually, that’s a lie. What it really does is much cooler: It gives scientists a way to create gamma rays the likes of which are currently impossible, something which they hope will provide new insights into physics problems such as — you guessed it — detecting dark matter.

And none of this would be possible without today’s modern AI techniques. The LHC produces an unfathomable one million gigabytes of data per second. Without deep learning networks to sort and sift through this data, the scientists may as well be searching for a needle in the universe’s biggest haystack.

Kazuhiro Terao, one of the physicists working on the gamma ray factory experiment, told a reporter from Stanford’s SLAC National Accelerator Laboratory:

Today we’re using machine learning mostly to find features in our data that can help us answer some of our questions. Ten years from now, machine learning algorithms may be able to ask their own questions independently and recognize when they find new physics.

Scientists hypothesized the existence of dark matter more than a century ago. And, even though it turned out the Milky Way’s bulge wasn’t the answer we were looking for today, we’re closer than ever to figuring out how to detect it.

And by “we,” I mean humans and machines working together.

Source: TNW

Well as of today’s standard most of what we see is now running by Artificial Intelligence like Tesla started self-driving vehicles, in medical field doctor is now replacing by AI-powered Robot doctors. And the biggest innovation in AI and Deep Learning will be the next level.

Dr Manahel Thabet was discussed in my previous article titled “Artificial Intelligence and the future of humans”Dr. Thabet mentions how Artificial Intelligence has been evolved.

The Deep refers as the number of the layers in artificial neurons in a network of Deep Learning. This is as an artificial nervous system with more layers of neurons that is more sophisticated kinds of learning.

Well by comparison of human brain vs Artificial Intelligences in year 2000s “human brain still more powerful than fastest computer and simplified that Internet complexity is just equivalent of entire Internet but this is not the end of battle between them, estimate by the computer Experts says Estimates around 2040 will AI will surpass the capabilities of Human Brain. Stephen Hawking has said, “The development of full Artificial Intelligence could spell the end of the human race.”

In term of storing information, well the AI still is on long way to go before surpassing the human brain capabilities, considering that DNA as memory. DNA hold much smaller than any computer Technologies. As of now, machine learning is now in reality like applied in Siri, Google, Netflix, or Amazon. Machine learning is now helping many companies to interpret data, find correlations and learn from missed forecast.

Now interaction like Siri is now near human, but still need to access a lot of information. Technology is always moving fast speed, well all of us should get ready for the disruptors that are coming on our world. Improving AI can seem scary but there a great opportunity that human can used to improve our world.

Trigo Vision, an AI startup founded by former members of the Israeli army’s special forces and intelligence community, just came out of stealth mode with a target in its sights: Amazon’s Go store.

When Amazon opened its cashier-less store in Seattle earlier this year, it was hailed as the future of brick-and-mortar shops. But, so far, there’s just the one. And even if the company follows through on plans to open half a dozen more, the locations will be little more than a novelty.

There are more than 150,000 convenience stores and 40,000 grocery markets in the US alone. Amazon may have enough money to build sophisticated storefronts full of expensive hardware from the ground up with no regard for profits – the marketing alone is worth it for the juggernaut worth $900 billion – but most other retail chains don’t. And mom-and-pop stores definitely can’t afford millions in R&D and installation costs. Luckily, they don’t have to.

Trigo Vision’s AI is designed to augment existing retail spaces. Through the use of regular cameras and advanced proprietary algorithms it’s able to accurately track shoppers and products in a way that facilitates cashier-free shopping experiences that, theoretically, could work in almost any retail environment.

The idea is to create a system that shopkeepers can integrate into their stores with minimal fuss. With Trigo Vision’s, there’s no need to rearrange products, spend days entering codes, or install sensors all over the place.

The benefits for shoppers go beyond the cool factor of skipping the long process of individually scanning each item to determine your total before paying. Depending on the retailer, customers will either confirm their purchases at kiosks positioned by exits, or simply log in with an app when they walk in, grab what they want, and then walk right out the front door letting the computers handle payment.

But, Trigo Vision is still perfecting its mix. Earlier this week it came out of stealth after successfully securing over $7 million during its first round of financing. Despite being a relative newcomer to the scene, however, its AI is already showing promise in tests with multiple clients.

We spoke with CEO Michael Gabay and COO Jenya Beilin who told us they’ve been developing the idea for years, even before Amazon launched its cashier-less Go store. Gabay told TNW the idea started as a smart shopping cart, but the team quickly realized it wasn’t the best solution.

It was too expensive, it broke a lot, people steal them, it cost retailers a lot of money. They have to replace batteries and do maintenance. That’s when we got the idea to put cameras overhead.

Trigo Vision’s team knew that, in order to make a product that appealed to most retailers, they’d have to come up with a solution that didn’t involve changing the existing infrastructure or requiring retailers to hire computer specialists.

Beilin told TNW that one of the biggest challenges was eliminating the bottlenecks that arise when implementing these kinds of systems. He says their AI doesn’t require store owners to manually input each product into a system, but instead it learns inventory as it goes. According to him:

For an 1800 square foot it takes a matter of weeks. Not days, yet, but also not months.

And, aside from the convenience of tracking purchases in real time, the cameras can also provide retailers with insights they’ve never had access to before. Beilin told us the AI gives retailers a bevy of data, but also respects consumer privacy:

Retailers get real time data of basically anything you can imagine. From shopping trends to the store’s inventory. But, we are dedicated to privacy, and we’re GDPR compliant. The privacy of our customers, and theirs, is very important to us. Our data is not personalized.

The team hopes to have a full-fledged product available for retailers by the end of the year, though at this point they aren’t putting a hard date on when it will officially roll out.

Source: TNW

SAYING GOODBYE. At 5:42 am EDT Friday, SpaceX launched a Falcon 9 rocket from Florida’s Cape Canaveral Air Force Station. Everything went according to plan; the only things that blew up were the ones that should blow up. At the time of writing, the rocket is helping its payload — the Dragon capsule — enter low Earth orbit. By Monday, it should rendezvous with the International Space Station (ISS), dropping off 5,900 pounds’ worth of supplies and gear in what’s now become a somewhat-routine mission for SpaceX.

While the objective itself isn’t necessarily anything out of the ordinary, today’s launch was special for SpaceX: it marked the last time the company would launch a Falcon 9 Block 4.

A BRIEF HISTORY OF THE FALCON 9. The Falcon 9 is SpaceX’s workhorse rocket, completing 55 successful missions since its initial launch in 2010. In December 2015, the launch of the Falcon 9 v1.2, also known as the Falcon 9 Full Thrust, kicked off the era of the reusable rocket.

SpaceX started launching updated versions of the first Falcon 9 Full Thrust, known as Block 3, in 2017, eventually landing on its Block 4 design. SpaceX had already announced in 2016 that it had plans for a Block 5 version, and the company successfully launched the first Falcon 9 Block 5 on May 11 this year.

A BETTER BLOCK. In a briefing with reporters just prior to the first Falcon 9 Block 5 launch, SpaceX CEO Elon Musk said it would be the final version of the Falcon 9. He also described the various ways it was an improvement over its predecessors.

Block 5 is about twice as powerful as the Falcon 9 that first launched in 2010, according to Musk. It is also far more reusable than the Block 3 and Block 4 versions. SpaceX has never launched a Falcon 9 rocket more than twice (though Musk estimates a Falcon 9 Block 4 could probably launch upwards of 10 times with enough maintenance).

In contrast, a Block 5 should be able to launch 10 times with no refurbishment — just like an airplane. With some refurbishment every 10 or so launches, Musk estimates the rocket could handle 100 flights. The potential cost savings for a rocket that reusable are staggering.

LOOKING AHEAD. Now that SpaceX is officially retiring Block 4, it can focus on completing the seven successful launches Block 5 needs for NASA to allow it to carry people. SpaceX’s goal is to knock those out by December so it can launch its first crewed Dragon capsule before the end of the year. So, while today’s launch marked an end to one era of SpaceX rocketry, it opens the door for us to enter the next.

READ MORE: SpaceX Is About to Launch Its Final Block 4 Falcon

Source: Futurism