The AI Times Monthly Newspaper

Curated Monthly News about Artificial Intelligence and Machine Learning

Major Server Upgrade for all Informed.AI sites

All,

We are very pleased to announce a significant migration of website hosting and server upgrade for all of our websites, including homeAI.info, Neurons.AI, Showcase.AI, Awards.AI, Events.AI and Informed.AI

This has been a major upgrade for us and has been executed very smoothly with zero downtime.

You should all experience much faster response and performance to all our websites making the user experience much more enjoyable.

As always we welcome your feedback

The Informed.AI Team


Link to Full Article: Read Here

Major Server Upgrade for all Informed.AI sites

Major Server Upgrade for all Informed.AI sites

All,

We are very pleased to announce a significant migration of website hosting and server upgrade for all of our websites, including homeAI.info, Neurons.AI, Showcase.AI, Awards.AI, Events.AI and Informed.AI

This has been a major upgrade for us and has been executed very smoothly with zero downtime.

You should all experience much faster response and performance to all our websites making the user experience much more enjoyable.

As always we welcome your feedback

The Informed.AI Team

 

What If We Live Too Long? Life Extension and the Problem of Population

It’s no secret that the world’s population is reaching crisis point – and still growing fast. Most developed nations are also encountering the problem of an ageing population, where there are more elderly people than those of working age. The fact is that people are living longer, and, if technology is to have its way (and it will), we will all be living even longer ourselves.

What Technology Is This, Precisely?

Well, as you’d expect, the main body of work in the anti-ageing space is in finding drugs that will stop the process of ageing in its tracks. From Google’s Calico, whose mission is to reverse engineer the biology that controls lifespan and “devise interventions that enable people to lead longer and healthier lives”, to other Silicon Valley players such as Human Longevity, which, in conjunction with StartUp Health, launched the Longevity Moonshot, and many more besides, intrepid warriors against death are working hard to reverse, reduce, and eliminate the effects of ageing and causes of death. All of the above centre on medical prevention through drugs.

There are, however, more mechanistic methods on the cards. Nanobots that are injected into your bloodstream that can identify health problems and mend them as they go along, infusing affected areas with drugs or performing tiny surgeries as part of their day-to-day lives, for example.

Then, of course, there’s the mind upload thing – the idea that we could all live forever as virtual avatars, in a world where disease would be eliminated because we would be free of biology altogether.

Artificial intelligence and robotics, as the technologies progress, have the potential for radical life extension by making us incrementally bionic until the faulty biological body is subsumed to the stronger, healthier bionic ideal. We can add bits on, chop bits off, until we’re happy with a body that will keep our conscious minds alive indefinitely. With the integration of robotic parts and artificial intelligence add-ons, we could also experience what renowned futurist, Ray Kurzweil, calls “life expansion” – where we don’t just live longer, we live in ways we had never before conceived, capable of doing any single thing we could possibly imagine. Sounds good, right?

The Drawbacks to Life Extension

Firstly, if we are all living longer and being healthier, then where on earth are we going to put everyone? In the UK alone, we are currently struggling to build enough new homes to meet the growing population – we fall short by several hundred thousand year on year. So what happens when we end up with more people than we can house or to provide the necessary infrastructure to support?

With more people living longer, the retirement age must necessarily go up. So, we’ll all need to be working for longer, too. We are also in the midst of an automation revolution. Artificial intelligence and robotics are threatening to destroy existing jobs. The solution proposed is that different roles will develop as a result, but will we be able to retrain such high numbers of workers for the new working landscape?

Not only will we have lots more people to house and to build infrastructure for, we will also have a large surplus workforce.

If we are talking about a future in which we can all live a terrifically long time, but few of us able to afford to support ourselves or to even live in a suitable home, then is it really worth it?

If a long life becomes a pleasure to be enjoyed by those with the means to do so, and the rest of us suffer and die just as we have always done, does that mean that financial wealth make one’s life more valuable? If wealth could be correlated positively with intelligence, creativity, innovation, morality, or even (arguably) beauty, then perhaps there would be an argument in favour of this eventuality. Unfortunately, the world just does not work like that.

So, What’s The Answer?

If we look back to the early days of the smartphone or the personal computer, we see that prices were phenomenally high. But things change. Tech becomes cheaper and more people have access over time. So there’s reason to believe that this will be the case for life-extension drugs and technology.

This does not address the problem of what we do with our excess population. Elon Musk reckons that colonising Mars is the solution – so we have two planets to spread ourselves over. And as our tech continues to evolve, we may be able to then colonise further into the galaxy. Sounds legit.

Feeding, housing, and employing a considerably large excess population, even if we are to colonise other planets, won’t be without its drawbacks, either.

Sure, we can automate food production even further than we already do, but we may have to make drastic changes to our diet in order to do so. One of the key aspects to this, regardless of tech or life extension, is to begin eliminating meat from our diets. It is a well-known statistic that 95% of Amazon rainforest destruction is down to cattle farming. Combined with the general health risks associated with red meat, we will need to stop eating cows.

This will bring cattle populations down, but will also allow these animals a better quality of life. There are lots of people hard at work on strategies to replace mammal meat in our diets with that of insects. Whilst that may gross a lot of people out, there is essentially no real problem with it. If we are to continue to thrive on this planet, we need to change our perceptions about how we live; how we eat is a central part of that. And insects may allow us to feed more people with less environmental impact.

As for housing, part of the problem is – arguably – our insistence on national borders. Rather than cosseting ourselves off from one another, a more even distribution of humanity should be considered. This will require another massive overhaul in how we live, but again, if we are to continue to thrive and to find a solution to overpopulation, we will need to let go of stubborn, xenophobic, outdated ideas and work together as a species.

The employment issue is perhaps the toughest one to answer. Whilst, invariably, new roles will emerge, we will need to have a radical rethink of how people sustain themselves. Universal basic income is one solution being posed, championed by Bill Gates and discussed at length by others.

However, all these solutions require us to completely revolutionise how we have been living for centuries. The world is still largely run by privileged, older, white people who are reluctant to see a world beyond that which they are used to. If we are to take the idea of living longer and better seriously, we are going to need a lot more innovative, broad-minded ideas to come to fruition.


Link to Full Article: Read Here

Is Artificial Intelligence Really the Next Technological Revolution?

Is Artificial Intelligence Really the Next Technological Revolution?

Is Artificial Intelligence Really the Next Technological Revolution?

A comparison of AI with previous technological breakthroughs

There’s no shortage of hype around artificial intelligence. Fueled by recent scientific advances in the field, AI is now characterized as the “new electricity”—a technological breakthrough that will revolutionize the world.

But are we sure that’s the case?

Many booms and busts have punctuated AI’s nearly half century of history. Excessive expectations and promises, which drove the first AI bubble in the 1980s, have been followed by decreased funding and interest — the so-called “AI winters.” But this time feels different. Five billion in venture capital was funneled into AI last year. Coupled with recent acquisitions of AI startups by tech companies such as Facebook, Google, and Apple, and the exploding interest by other companies — reflected in the skyrocketing mentions of AI in company earnings calls — it seems rather obvious that AI is here to stay.

But is AI indeed the next major technological revolution? Is there a generic structure of technological revolutions that can be identified historically? If so, can the insights of previous technological revolutions be applied to AI? And if AI represents a major technological breakthrough that is comparable to electricity and steam, in which phase of its development do we currently find ourselves?

In her work on the economics of innovation and technological change, socio-economist Carlota Perez has traced the discontinuities and regularities in the process of innovation. Similarly to Thomas Kuhn’s work on the nature of scientific discoveries — in which scientific revolutions disrupt the process of science and trigger the formation of new scientific paradigms — Perez identifies a sequence of technological revolutions and “techno-economic paradigms” that have disrupted our industries and societies.

A technological revolution — which locally disrupts a specific market or industry in terms of new inputs, methods, and technologies — becomes a techno-economic paradigm when it starts to globally transform organizational structures, business models, and strategies in markets and sectors beyond which the technological breakthrough had initially erupted. Techno-economic paradigms, in other words, represent a collectively shared best practice model of the most successful and profitable uses of the new innovations. By enabling the wide-spread diffusion and adoption of the emerging technologies across economies and societies, techno-economic paradigms will fundamentally affect our socio-institutional frameworks.

As Perez has shown, two distinct phases can be identified in each technological revolution. There is an “installation” phase, in which innovators and entrepreneurs explore the potential of the new technology. In this phase, the diffusion of a breakthrough technology is often driven by a financial bubble. The installation phase is followed by a “turning point” or phase of readjustment — in which the bubble bursts — and the “deployment” period, which diffuses the new technological system across industries, economies, and societies.

Each technological revolution can be characterized further in terms of a specific life cycle, which, as Perez documents, tends to last around half a century (see image 1). Perez identifies four distinct phases within such a life cycle: an initial period, which is characterized by explosive growth and innovations and new products; a phase of constellation, in which new industries, infrastructures, and technology systems are built out; the full expansion of innovation; and the last phase, which is defined by technological maturity and market saturation.

Perez defines a technological revolution as a set of interrelated radical breakthroughs — that is, singular innovations — that form a constellation of interdependent technologies. A technological revolution, in other words, is a cluster of clusters, or a system of systems of technological innovations. The recent major breakthrough in information technology, for example, formed such a technology system around microprocessors and other integrated semiconductors, from which new technological trajectories opened up: personal computers, software, telecommunication, and the internet emerged from the initial technological system. These new technological systems subsequently created strong inter-dependence and feedbacks between technologies and markets. The defining features of technological revolutions — as opposed to a random collection of singular innovations — are thus the following: (1) they are interconnected and interdependent in their technologies and markets, and; (2) they have the disruptive potential to radically transform the rest of the economy and society.

Historically, Perez identifies five such major technological meta-systems, which were initially triggered by a technological (or scientific) breakthrough and, then, expanded across industries and economies. The first such disruption of the late 18th century was organized around the mechanization of factories, water power, and the canal networks. This was followed by the second revolution, which initiated the age of steam and railways. In the late 19th century, electricity, steel, and heavy engineering intensified international trade and globalization. In the last century, two technological revolutions transformed our economic and industrial system: the age of oil, mass production and the automobile was followed by the era of information and communication technology.

What made these technological disruptions revolutionary were not only the new interrelated technologies, industries, and infrastructures but their transformative potential defined in terms of extraordinary increases in productivity that they enabled. When a technological revolution propagates across industries and economies, it radically transforms the cost structure of production by providing new powerful inputs (such as steel, oil, or microelectronics). Thereby, it unleashes new innovations and interrelated technological systems, which renew existing industries and create new ones.

Perez provides a powerful framework that can be applied to the current state of AI. Given Perez’ conceptual model of the diffusion of technological innovations, the new AI industries and systems that are forming now can be located between phase one, the period of “paradigm configuration,” and the phase of “full constellation,” in which new industries emerge and infrastructures get installed. The explosive growth and innovation we are experiencing at the moment typically characterizes phase one. While new industries, technological systems, and infrastructures emerge in phase two — which results in intensified investment and market growth — the technological revolution is transforming its core industries, but has not yet permeated economies and societies as a whole.

While the recent extraordinary investments in AI might lead to another bubble — which might indicate the “turning point” or phase of readjustment in Perez’ model — it seems that the economic space today is, indeed, different than during the last AI bubble (or, perhaps, the bursting of the first AI bubble in the 1980s already marked the “turning point” — over-inflated expectations crashed when cheap UNIX workstations triggered the fall of over-priced expert systems running on LISP and the Dreyfus brothers published their Mind over Machine, which undermined some of the pretentious and flawed assumptions of the first generation of AI research). Not only has there been massive growth in computation, GPUs, storage, datasets, user demand, high levels of R&D, and VC investment, but governments have also started novel AI initiatives. The UK government recently announced increased funding for AI research; the Chinese government gave AI priority status in R&D and commercialization; and the US government has funded AI research last year with more than $1 billion.

The role of public R&D is singularly important for technological revolutions as the previous five major technological surges have all been, to some extent, government-sponsored (such as the canals and railways networks, or the Internet, which has been heavily funded by government agencies such as DARPA). Historically, the synergistic financing of governments and financial capital, such as venture capital, has been crucial for the diffusion and adoption of technological breakthroughs and their consolidation into techno-economic paradigms.

But in what sense, then, does AI share the features of the previous technological revolutions that can be historically identified? The emerging AI technology systems clearly exhibit the interconnectedness and interdependence in their technologies and markets, which characterize the previous technological revolutions. AI represents not just another new dynamic industry that is added to the existing production structure; rather it provides the means to modernize almost all existing industries and activities. New AI-powered industries and infrastructures are forming at the moment that not only fundamentally re-organize existing industries, but have started to deeply affect organizational structures, business models, and strategies. As it was the case with steam and electricity, these technological and scientific breakthroughs are not only productivity-enhancing in the core industries but are beginning to permeate various peripheral sectors and markets.

In this sense, AI has all the features of what economists call a general purpose technology (GPT). In economics, a GPT is defined as a generic technology, which (1) can be improved, (2) can be widely used and applied, and (3) expands the space of possible innovations and investments. Similar to historical GPTs, such as the steam engine, electricity, or microelectronics, these new interconnected and interdependent AI-based technology systems and markets have not only the potential to enable innovations in products, processes, and organizational structures — as previous GPTs did — but also to radically transform our economic, social, political structures.

AI has all the defining features of previous technological revolutions — it is becoming a cluster of interrelated generic technologies and organizing principles that are starting to spread far beyond the confines of a specific industry. At the core of all the previous technological revolutions has been an all-pervasive low-cost input, often a new material or energy source combined with novel products, processes, and infrastructures. Similar to electricity, steam, or microelectronics, AI — fueled by GPU-accelerated computing, massive increases in available data, and drastically reduced costs — seems to be on the cusp of becoming such a cheap and ubiquitous new input. Similar to steam in the 18th century or electricity today, distributed AI could soon power almost all products and processes and deeply permeate existing and novel infrastructures and industries.

Indeed, AI could become the “new electricity.” We are not there yet. But given Perez’ model of diffusion and adoption of technological innovation, we may indeed be at the cusp of a revolution.

AI STARTUP SHERPA WINS THREE PRESTIGIOUS AWARDS IN TWO MONTHS

AI STARTUP SHERPA WINS THREE PRESTIGIOUS AWARDS IN TWO MONTHS

STARTUP SHERPA WINS THREE PRESTIGIOUS AWARDS IN TWO MONTHS

  • SHERPA has been awarded three major awards in the past two months. The Red Herring 100 Winner Europe 2017, the White Bull Award and the Best Mobile App Award
  • SHERPA was a finalist at CognitionX, an awards ceremony in London which recognises excellence in Artificial Intelligence, where it was surpassed only by Deep Mind (Google)
  • SHERPA has also been featured by the technology consultancy GARTNER as one of the top three intelligent apps of the moment

 

Bilbao, June 29th, 2017 – SHERPA, the predictive Artificial Intelligence platform, has been recognised as one of the best European startups, winning three major awards in the past two months. SHERPA, a personal assistant which integrates with smart devices, has been awarded the Red Herring Europe Winner 2017, the White Bull Award 2017 and the Best Mobile App Award.

The Red Herring Top 100 Europe celebrates the top private companies in the European region. Red Herring’s editorial team analyses hundreds of cutting edge companies and technologies and selects those that are positioned to grow at an explosive rate.

The White Bull Awards, held in association with the multinational Qualcomm, bring together Europe’s top technology and media leaders, entrepreneurs, innovators, investors, and visionaries. They select the best startups in Europe based on the criteria of innovation, leadership, growth and potential for growth.

CognitionX, in association with the Alan Turing Institute, awards the best and most innovative contributions to Artificial Intelligence of the year in a ceremony in London. SHERPA was shortlisted in the Best Innovation in Artificial Intelligence category, surpassed only by Google Deep Mind.

Following on from these successes, SHERPA’s mobile app has won the Best Mobile App Interface Award.

As a result of the above and because of SHERPA’s new algorithms, SHERPA’s app has also been recognised by GARTNER (the prestigious technology consultancy) as one of the three intelligent apps of the moment.

These aren’t the only awards SHERPA has been given recently. In December 2016, SHERPA won the prestigious Digital Top 50 Award. The Digital Top 50 Awards were founded by Google, McKinsey y Rocket Internet to recognise and reward bold talent, cutting-edge innovation and sharp business acumen amongst the most promising European startups.

 

About SHERPA

SHERPA is the start-up responsible for creating the Predictive Personal Assistant that directly competes with those developed by Google and Apple. With headquarters in Bilbao, the platform is based on the most advanced technology and algorithms in Artificial Intelligence.

In 2015, SHERPA reached an agreement with Samsung to install its software in the Korean company’s devices, last year SHERPA raised $6.5 million in a Series A round of financing.

 

You can find more information on SHERPA here: http://sher.pa

 

Related links:

 

For more information (press): Text100 – Virginia Huerta (Virginia.huerta@text100.es) +34 91.561.94.15 / press@sher.pa

Artificial Intelligence Startup Biggerpan Welcomes World-Renowned Researcher Dr. Gregory Grefenstette as Chief Scientific Officer to Lead its Predictive Technology Works


Artificial Intelligence Startup Biggerpan Welcomes World-Renowned Researcher Dr. Gregory Grefenstette as Chief Scientific Officer to Lead its Predictive Technology Works

A scientific expert and reputed technology leader, Dr. Grefenstette will lead research and development efforts of the world’s first predictive artificial intelligence for the mobile web

 

SAN FRANCISCO, June 13, 2017 /PRNewswire/ — Biggerpan, a startup company which develops a predictive artificial intelligence (AI) technology that anticipates people’s needs on mobile, is pleased to welcome Dr. Gregory Grefenstette as Chief Scientific Officer. A world-renowned expert, Dr. Grefenstette brings more than 30 years of experience in the AI industry, and is considered a leading researcher in the field of natural language processing (NLP) and information retrieval. In his newly appointed role, he is responsible for driving the continued research and development of Biggerpan’s breakthrough technology.

Biggerpan’s mission is to make the Internet smart on mobile by building the first AI that predicts what you want, so you don’t have to search. “As we are shifting away from the traditional keyboard and mouse paradigm, people will need to rely more and more on predictive interfaces,” said Eric Poindessault, co-founder and CEO at Biggerpan. “Today we target the mobile user experience, where 75% of the Internet use is happening right now, tomorrow think virtual and augmented reality.”

Derived from the latest research in natural language processing, a branch of AI which extracts meaning from text, Biggerpan’s proprietary technology is able to analyze and understand any web page in real time in order to make the most relevant recommendations. For example, if you are reading an article about a movie, it instantly offers you to watch the trailer or to buy tickets online. Biggerpan’s technology takes text comprehension to new heights, as it understands the meaning of each word based on the context of an entire page rather than just the surrounding words, which allows for a more effective disambiguation. It goes further thanks to a unique multi-class entity recognition approach which allows it to identify topics almost instantaneously.

Dr. Gregory Grefenstette joins the team as an authority in natural language processing, as he has continuously been pioneering the fields of cross-language information retrieval and of distributional semantics, the induction and extraction of meaning from large quantities of text. Sought after as a keynote speaker, he is named inventor in 20 granted U.S. patents, has authored and edited four books, and published hundreds of research papers in the most prestigious scientific journals. Dr. Grefenstette previously held chief scientific officer positions at Xerox Research Centre Europe, search engine company Exalead, Clairvoyance Corporation and with the French CEA, and was a senior researcher at numerous top-tier institutions such as INRIA and the Florida Institute for Human & Machine Cognition (IHMC). A graduate from Stanford University, Dr. Grefenstette initially studied mathematical sciences at the Massachusetts Institute of Technology and later received a PhD in computer science from the University of Pittsburgh.

“Today, the power and capacity of a computer is underused. We can leverage algorithms to provide powerful predictions, avoiding the frustrations of typing and searching on a small device,” said Dr. Gregory Grefenstette. “I am excited to be part of a team that is at the forefront of such innovation and look forward to incorporate my years of research into a useful real-world application through the development of this technology.”

Dr. Grefenstette now brings an unrivaled level of expertise and experience to Biggerpan. His past work and interests acutely align with the company’s forward-thinking vision, fostering ideal conditions for future enrichment of the technology, and overall company success.

“We are very happy to welcome Dr. Gregory Grefenstette to the team,” said Eric Poindessault. “As a founding father of modern NLP, his extensive knowledge and experience will accelerate the development of our AI technology and propel us forward in achieving our mission.”

ABOUT

Biggerpan is a French-American startup which develops a predictive artificial intelligence that leverages context to make real-time recommendations. The company’s mission statement is to build a brain for the mobile web, to allow a better integration of the technology into our lives, without all the pain and frictions that are found in traditional mobile online activities.

The first product released by Biggerpan is Ulli, a smart mobile web browser that simplifies the experience on a mobile device by recommending the most relevant content, services and purchases for people to navigate based on the context of their current browsing. The iOS app was nominated 2016 Mobile App of the Year by Product Hunt and featured on the Emerging Tech Tour at Mobile World Congress 2017.

Visit www.biggerpan.com for more information including a video.

CONTACT

Luc Hancock
1-415-867-4031
luc@biggerpan.com
facebook.com/biggerpan.inc
twitter.com/ulliapp

LONDON DEEP LEARNING IN RETAIL & ADVERTISING SUMMIT, DAY 1 HIGHLIGHTS

Original

The use of deep learning in retail and advertising, is rapidly expanding and becoming an integral part of consumerism.

We’re almost at the end of day 1 of our Deep Learning in Retail and Advertising Summit in London, and we’ve brought together data scientists, engineers, CTOs, CEOs and leading retailers to explore the impact of deep learning and AI on the industry.

Deep Learning Trends & Customer Insight


Ben Chamberlain, Senior Big Data Engineer from ASOS kicked off this morning’s discussion by exploring the impact that deep learning has in predicting the customer lifetime value in e-commerce (CLTV).

Deep learning works really well for deterministic tasks, and as CTLV is absolutely not a deterministic task, it’s extremely difficult – I don’t even know my own value to ASOS, it’s a very different kind of problem.

In an ideal world, ASOS would ‘know every action a customer will make for the rest of time, but [they] can’t do that’. Identifying and distinguishing between high and low value customers allows companies to optimise market spend and minimise exposure to unprofitable customers. He explained how ‘a large percentage of customers churn and have a 0% CTLV, whilst some will spend millions each year on ASOS.’ This makes machine learning incredibly complex to implement, and when it was first implemented, it was ‘wrong for the vast majority of customers’. To overcome this, Chamberlain explained how they tested two models: the widened deep model, and the random forest model with neural embeddings which combines combines automatic feature learning through deep neural models with hand-crafted features to produce CLTV estimations that outperform either paradigm used in isolation. Model two has some merit, and this implementation of deep neural networks was tested and adopted by ASOS.

Hear more from ASOS:
@b_p_chamberlain: Our new paper on neural embeddings in hyperbolic space. http://arxiv.org/abs/1705.10359  Talking about this at #reworkretail in LondonIn a CTLV paper published by ASOS, they expand on the implementation of this model, following on from the success ‘of DNN’s in vision, speech recognition, and recommendation systems’, which was influenced by Yann LeCun, Yoshua Bengio, and Geoffrey‚ Hinton’s 2015 paper, Deep Learning, (2015). Hear from this trio of innovators ahead of their appearance at our Deep Learning Summit Montreal where they have just been announced as the Panel of Pioneers, check out our blog post here.

 

Forecasting & Recommendations

The accuracy of probabilistic forecasts is integral to Amazon’s business optimisation process, and machine learning scientist, Jan Gasthaus spoke about the methods they are currently using.

Why bother forecasting?

If I knew the future, I could make optimal decisions. However, I don’t know the future. But what’s the next best thing? Creating accurate predictions taking into account past data to capture the uncertainty and predict more accurately. This allows me to give estimates and quantify them.

People are currently using what Gasthaus called ‘the onion approach’ where ‘you peel away bits you understand and you’re left with the part of the problem that the probabilistic time series can digest.’ Whilst this method has several pros such as its de-facto standard and decomposition, there are also several obstacles such as the amount of manual work required as well as its inability to learn patterns across time series. For example, Gasthaus explained how it is problematic if they ‘care about the forecast in a three week window for example rather than a specific day.’

To overcome this, Amazon are using black box deep learning functions to from simpler building blocks and learn them end to end to come up with their model of distribution and train neural networks. This novel method they are proposing  to produce accurate forecasts is DeepAR. This is ‘based on training an auto-regressive recurrent network model on a large number of related time series.’ Here, the input is the time series of past values, and the output is the estimated joint distribution. ‘Deep learning methodology applied to forecasting yields flexible, accurate, and scalable forecasting systems where models can learn complex temporal patterns across time.’

Dr Janet Bastiman‏ @Yssybyl: @Dr Janet Bastiman @Yssybyl: Predicting the future @amazon by Jan Gasthaus with #DeepLearning – comparison to past approaches #ReworkRETAIL

We next heard from Rami Al-Salman who explained how Trivago are ‘using a culmination of artificial neural networks, word embedding, deep learning and image search to optimise the results that users receive for each distinct search.’ Trivago serves millions of queries every day, and one of the biggest challenges is ‘predicting the intention of users queries’ and providing appropriately corresponding recommendations, for example ‘when a user types czech + currency we will want to recommend “koruna” as an additional search keyword’. This word embedding and use of is ‘one of the most exciting topics nowadays, as it learns a low-dimensional vector representation of words from huge amounts of unconstructed data as well as capturing the semantics of data.’ Deep learning methods are progressing so rapidly in natural language processing and computer vision, Trivago have applied these advancements to their model to provide an improved user experience. Trivago took ‘6 million hotel reviews and put them through vectors, and it’s possible to use word2vec because it’s scalable. Where a normal categorisation would take days to produce, word2vec produces results in less than 30 minutes.’  Al-Salman explained that word2vec allows them to learn the representation of words and give accurate suggestions. He also revealed that in the near future, deep learning will be applied to classify hotels to provide better search facilities.

Hear more from Trivago:
DeepTags: Integration of Various VGI Resources Towards Enhanced Data Quality
Warehouse & Stock Optimisation

Calvin Seward from Zalando spoke today about two issues they are working to overcome in warehouse and stock optimisation: the picker routing problem, and the order batching problem. In a physical warehouse there are rows and aisles of stock and ‘you’ve got a bunch of locations in the warehouse that pickers have to visit – it’s super inefficient.’

One you come up with an optimal route, you can drive efficiency and save money. That’s the goal of our project.

To overcome this problem, research scientist Seward explained how they developed the OCaPi (Optimal Cart Pick) Algorithm to calculate the optimal route to walk. This algorithm however, still has a runtime of around 1 second.

The second problem of order batching lies when ‘customers have ordered a bunch of things. We want to split these into different pick tours, but we can’t assign one order to multiple pick lists because there’s no way to bring the order together.’

By implementing a group force optimisation strategy, Zalando saw an 8.4% increase in efficiency. Additionally, Seward explained that by using neural networks, they can estimate the pick route length and by combining this with OCaPi they have created ‘a black box strategy where we get the neural network to learn from the examples’ which is a whole lot faster. With only the OCaPi running the results were never better than 0.3 seconds, but on the same CPU with the addition of the neural network approach it gets down to milliseconds.

Hear more from Zalando:
Zalando are using AI in several aspects of their business and have also created Fashion DNA to make the properties of their products more accessible, by collecting disjointed information in their catalog and mapping it into an abstract mathematical space – the “fashion space”. We spoke to Roland Vollgraf, Research Lead at Zalando Research, who expanded on this.Check out the interview here.

Couldn’t make it to London?

Register for on-demand post-event access to receive all the slides, presentation videos and interviews from the summit, or check out our calendar of upcoming events here.

View our upcoming events calendar and register here.

HOW ONE BOSTON STARTUP IS OVERCOMING FLIGHT DELAYS AND CANCELLATIONS

Original

Over the last year, Boston has seen its tech scene flourish, and when our team headed out for the Deep Learning Summit and the Deep Learning in Healthcare Summit at the end of May, we heard first hand from some of these thriving companies.

Having traveled back from Boston on the day of the British Airways IT meltdown, it really drew on the functionality of Freebird, a B2B travel tech startup based in Boston, and how it could have saved the day had we been booked onto a BA flight back to the UK.

@getfreebird: British Airways outage impacts 75K travelers and £100m costs. Freebird travelers rebook next flight on any airline.

Everyone knows that sinking feeling when you’re waiting at the airport and your flight flashes up ‘delayed’. You wish you’d got to the airport a couple of hours later, or on the occasion of cancelled flights not bothered at all, but how do we know ‘which of the over 30 million commercial flights in the US will get actually delayed or cancelled?‘ Freebird has built a business based on using data science to answer that question, their number one priority is to eliminate the stress, delay, and massive inconvenience delays can cause – they know that ‘getting there matters’.

Sam Zimmerman, CTO & Co-founder spoke at the Deep Learning Summit Boston last week where he explained that the Freebird team have created a real-time predictive analytics engine based on dynamic data sets and deep-learning algorithms. In the event of a cancellation or severe delay, with Freebird you can skip the line and instantly book a new ticket (on any airline) at no extra cost.

But how does this work?

Freebird started out with the intention of serving the B2C market, and after a successful incubation period realised that the ‘corporate market needed something for travel agents to better take care of their passengers, which was one thing that [they] had already validated’ with the B2C model. For companies to get their team to meetings or conferences in a limited and often tight time schedule is often of paramount importance, and there was an obvious gap in the market and a need in the corporate space for a tech solution to these travel inconveniences. Not only are disruptions bad for business, but each year they have an annual cost of $60 billion, and the US has a travel insurance spend of over $3 billion.

‘We can’t stop flights from being cancelled, but we’re doing the next best thing’.

Screenshot 2017-06-06 170525png

Zimmerman spoke about the multitude of data the platform amalgamates in order to compute prices correctly and calculate the appropriate quotes. The ‘cutting edge predictive analytics tool takes into consideration weather data, flight pricing and availability, to price the booking solution and to inform companies of the micro risk their travellers are facing every day.’ Using these dynamic data sets they are able to construct and train deep learning algorithms to generate an accurate output determining the likelihood of these disruptions. He explained are not an insurance company, but a technical company solving a technical problem to help improve the industry by buying ‘low cost last minute airline tickets that typically go unsold.

Although Freebird began its life as a self contained mobile app, it is now a multi-platform agnostic medium that sends disruption notifications via sms, email, or the airline carrier’s app to notify passengers as promptly as possible about any delays. This is so that the app can be integrated with the travel agents’ systems and provide a smooth user experience that doesn’t require any additional software from the passenger.

@pradipt: #Flight #Disruption #Startup Freebird Snares #Funding from General Catalyst and Accomplice

Want to hear from more companies working with Deep Learning? Our next Deep Learning Summit is in London 21-22nd September.
Confirmed attendees include: Google Deep Mind, Facebook AI Research, Jukedeck, Aplha-i, Facebook, OpenAI

GoCompare launches a search for the sharpest data experts

GoCompare launches a search for the sharpest data experts

GoCompare, the comparison website based in Newport, south Wales, has partnered with technology consultants, Kubrick Group, to launch a graduate challenge to find the data leaders of the future. Successful applicants will secure a fully-paid place on Kubrick’s coveted 18-week big data engineering training course, followed by the potential for a two-year data science development programme at GoCompare.

 

The challenge, which is open for applications until 10 July, forms part of GoCompare’s ambitions to become the tech employer of choice in the region.

 

Jackson Hull, chief technology officer at GoCompare, said: “We’re committed to developing world-class talent in Wales, and through this challenge we want to inject a bit of fun into the recruitment process, with the significant incentive of a paid-for training course and the chance to secure a position at GoCompare for the successful applicant or applicants.

 

“The challenge is open to recent graduates and those in the early stages of their career who want to pursue a new opportunity. Anyone who is confident with mathematics, and is familiar with data and how it can be used in real-life applications to make people’s lives easier, is encouraged to take part in the challenge.”

 

Jackson continued: “For those who make the cut, there’s a place on an in-demand 18-week course in London provided by Kubrick Group, the technology consultants, who will pay them a salary while they train. And we’ll even contribute to their living expenses on top of this.

 

“Successful candidates will be employed as consultants by Kubrick Group and will have the chance to join GoCompare on a two-year data science development programme, working on exciting and ground-breaking projects alongside a skilled, dedicated and supportive team.

 

“Top performers of the data science development programme will be offered a full time role at GoCompare and given the on-going support they need to pursue a hugely rewarding career at the cutting edge of data science.

 

“If this appeals to you, I’d encourage you to apply today.”

 

Simon Walker, managing partner at Kubrick Group, added: “It’s really exciting to work with GoCompare as they move to using data science to improve their customer experience. It’s great to see they are utilising Kubrick’s training to help them achieve this.”

 

Anyone interested in applying can do so here: www.gocompare.com/data-science/

The Future of Investment in AI Could Be Decided By AI Itself

Many believe that artificial intelligence is set to reshape global economies and society, with AI expected to double economic growth. But what are the opportunities and challenges to investing in the current world of AI?

In 2016, the AI market was worth just $644 million, according to Tractica. This year that amount is due to almost double and continue to grow exponentially, with predictions showing it to reach $36.8 billion by 2025. With so many pioneering companies and world-leaders focusing their attention and funding on artificial intelligence fields such as deep learning and machine intelligence, the momentum for progress is well underway.

At the Machine Intelligence Summit on 28-29 June, Julius Rüssmann, Analyst at Earlybird Venture Capital, will share expertise on a panel exploring the challenges and opportunities of investing in AI. Julius believes investors themselves will be disrupted by the AI revolution. Could future investments in AI be decided by other artificially intelligent systems instead of humans? I spoke to him ahead of the summit to learn more.

What are in your opinion the most promising machine intelligence sectors to invest in?

Broadly defined, the service and manufacturing industries will probably benefit the most from (an increased level) of Machine Intelligence. By that I refer to the idea that large parts of the service industry, today still based on human work, can be heavily digitized, automatized and even improved through intelligent software applications. Especially, when considering the fact that the complexity inherent to each and every service inquiry increases, exponentially as data growth, machine intelligence is critical to ensure smoothly running system.

Besides primarily consumer focused service applications, the manufacturing industry will not remain the same. As machine intelligence develops, the sharp line between human-based work and robotics vanishes (think of collaborative robotics) until robotics will effectively overtake the bulk share of manufacturing processes and frees up billions of hours every day that have been had allocated to human work beforehand (problems that will arise).

One good example of Machine Intelligence that will redefine industries and humans alike is enriched or contextual computer vision. If it would be possible for software to contextually accurately understand video content, that would change a lot (autonomous driving, health care and so forth).

Besides industries and application fields, we deem Deep Reinforcement Learning as well as Neural Networks to be critical to facilitate further use-cases of Machine Intelligence.

What are the characteristics you are looking for in a startup prior to investing?

First and foremost, we look at the people behind the startup. Why is that? Because we think that complementary skills and solid commitment are in every successful company the kea driver. Starting a company, especially in the field of technology, always implies that there will be tough times and complex problems to be solved. In those situations it is almost irrelevant, how attractive you business model or the targeted market is. It is all about the team, to steer and pivot the company in the right direction (again). Besides outstanding people, we like to see early product (prototypes) to see credibility on execution, management and skills. In most cases that turn out to be successful you will see some sort of market adaption or commercial traction already early on, as customers and markets grave for such a solution and are open to use the new offering (think of solving a real problem.

How will venture capitalists be impacted by machine intelligence in the next 5 years?

Venture capitalists (VCs) will face a two-sided effect. First of all deal flow will significantly increase in the area of Machine Intelligence-based companies that are able to produce and deliver a solid value proposition to the market and are truly disruptive to specific industries. Today we still see a lot of evolutionary Machine Intelligence applications compared to revolutionary business models, many cases we see are rather a MI feature set, but not a standalone business case or company – this will change.

VCs will be threatened and potentially even disrupted themselves by MI. In essence, VC is also just a service industry (we service our portfolios and LP’s) and evidence clearly suggests that advanced MI will reach better (investment) decisions than humans do. However, the question remains how long this development will take; and yet, there is no clear evidence that MI will be capable of assessing or completely understanding humans. Considering, what has been stated in the beginning, namely that team are key, it’s not clear whether MI will necessarily reach better decision.

What are the dangers of no distinguishing between hype and reality in AI?

As explained above, AI/ML/MI are today quite advanced but not ultimately “ready” yet. This means that a lot of application fields (e.g. customer service industry) can clearly benefit from the introduction of smart algorithms (get more efficient, partially replace humans, better results, faster etc.), but they are not yet ripe for disrupting those industries effectively. So the danger is, from a VC’s or Founders perspective, to overestimate the capabilities of the algorithm, or to underestimate the importance of human-based decisions and verification. Effectively, there is lot of (technical) work still to be done and markets are only about to open up or be created for AI application. Time to market is critical for building up good investment cases.

Do VCs have a role in progressing the fields involved with AI?

VC’s, as with every other technology field, are responsible in finding and funding the leading teams/brains/companies in the respective field. This work is critical to contribute to the technology’s further development, to facilitate market adoption, to help identify viable business models and so forth (offering capital to outstanding entrepreneurs will improve the economy and the startup ecosystem in any ways). By finding and funding good technology companies in the AI field, VC’s also help to steer public attention to this area and to help create flagship project that then attract more brain-power and top-talent. As state before, it also in the responsibility of VC’s to be critical in their decision process also in order to prevent over-hype and bubble effects. It is sort of an educational responsibility that VC’s have for the tech ecosystem, the economy and the society.

Julius Rüssmann will be speaking at the Machine Intelligence Summit, taking place alongside the Machine Intelligence in Autonomous Vehicles Summit in Amsterdam on 28-29 June. Meet with and learn from leading experts about how AI will impact transport, manufacturing, healthcare, retail and more.
Other confirmed speakers include Roland Vollgraf, Research Lead, Zalando Research; Alexandros Karatzoglou, Scientific Director, Télefonica; Sven Behnke, Head of Autonomous Intelligent Systems Group, University of Bonn; Damian Borth, Director of the Deep Learning Competence Center, DFKI; Daniel Gebler, CTO, Picnic; and Adam Grzywaczewski, Deep Learning Solution Architect, NVIDIA. View more speakers and topics here.
Tickets are limited for this event. Register to attend now.

Opinions expressed in this interview may not represent the views of RE•WORK. As a result some opinions may even go against the views of RE•WORK but are posted in order to encourage debate and well-rounded knowledge sharing, and to allow alternate views to be presented to our community.


Link to Full Article: Read Here

BOOTSTRAPPING AN INTELLIGENT RECOMMENDER SYSTEM

In many different web services, machine learning is being used for recommendation systems that help users tackle information overload: there are simply too many movies, songs, and books for users to usefully browse through. Without such tools, some services are rapidly falling behind and losing customers.

Travel is a little bit different, as the world does not have millions of cities, but finding new, interesting places to travel to is still a challenge. Years ago, Skyscanner started it’s ‘everywhere’ search which allows users to find the cheapest places possible that they could travel to, leading to research showing that price is one of many factors that make a place attractive and interesting.

Neal Lathia, Senior Data Scientist at Skyscanner, will join us at the Machine Intelligence Summit in Amsterdam, to share how the company bootstrapped a destination recommender system using rich implicit data generated by millions of users, along with simple algorithmic approaches, and experiments that gauge how localised and personalised recommendation affects user engagement. I spoke to Neal ahead of the event to learn more.

Please tell us more about your work at Skyscanner.

As a Senior Data Scientist, my focus is on designing and building machine learning features for Skyscanner’s mobile app. Since joining, just under a year ago, the projects I’ve been working on have related to recommendation and search result ranking. However, the app creates a very rich ecosystem of data, and we have already identified a number of other opportunities ahead.

What do you feel are the leading factors enabling recent advancements in machine learning for recommendation systems?

Many of the near state-of-the-art algorithms for recommendation systems have been open sourced- which is always welcome news! The research field has also always been driven by open data challenges. Most importantly, the research community has always taken a multidisciplinary approach – not all recommender system challenges need to be solved with machine learning.

Which industries have the biggest potential to be impacted by advancements in recommendation systems?

As someone who has a background in recommender systems, it is difficult for me to try to envisage any industry without the lens of recommendation potential. There are so many facets of life where personalised information could be useful – from healthcare to travel and beyond.

What developments can we expect to see in machine intelligence in the travel industry in the next 5 years?

Many of the best known travel sites online have a distinct focus on price – helping users find the cheapest flight, hotel, or car (Skyscanner is no exception to this!). As these services gain greater smartphone traction, and data (e.g., flight statuses and prices) becomes available in real-time, the travel industry is going to become a ripe domain for machine intelligence applications.

Outside of your own field, what area of machine learning do you will see the most progress in the next 5 years?

There is no doubt that recent advances in neural networks have lead to wonderful results in the areas of reinforcement learning and machine vision – I expect that progress to continue to accelerate. I’m looking forward to interesting products that may arise from these areas of research.

Neal Lathia will be speaking at the Machine Intelligence Summit, taking place alongside the Machine Intelligence in Autonomous Vehicles Summit in Amsterdam on 28-29 June. Meet with and learn from leading experts about how AI will impact transport, manufacturing, healthcare, retail and more.
Other confirmed speakers include Roland Vollgraf, Research Lead, Zalando Research; Alexandros Karatzoglou, Scientific Director, Télefonica; Sven Behnke, Head of Autonomous Intelligent Systems Group, University of Bonn; Damian Borth, Director of the Deep Learning Competence Center, DFKI; Daniel Gebler, CTO, Picnic; and Adam Grzywaczewski, Deep Learning Solution Architect, NVIDIA. View more speakers and topics here.
Tickets are limited for this event. Register to attend now.

Opinions expressed in this interview may not represent the views of RE•WORK. As a result some opinions may even go against the views of RE•WORK but are posted in order to encourage debate and well-rounded knowledge sharing, and to allow alternate views to be presented to our community.


Link to Full Article: Read Here

CREATING A MORE EFFICIENT SHOPPING EXPERIENCE WITH DEEP LEARNING

Retail is undergoing an artificial intelligence revolution. The latest advancements in deep learning algorithms are now impacting all corners of the industry, from stock optimization and smart warehousing, to search, recommendation systems and forecasting.

These significant advancements in AI, data science and deep learning are giving online shoppers and sales staff the tools they need for the most the efficient shopping experience possible. At Instacart, these technologies are allowing them to predict the sequence that customers pick items in specific store locations, in some cases saving their product pickers upwards of 10% of their time spent locating and gathering items in-store. This efficiency is extremely valuable in the competitive and continually evolving world of online shopping.

At the 2017 Machine Intelligence Summit in San Francisco, Jeremy Stanley, VP of Data Science at Instacart, shared expertise on how deep learning can be used to create more efficient online shopping, with insights into the data collection, mobile technology and machine learning approaches they are applying to enable on-demand grocery delivery. View his presentation with slides below.

Instacart has revolutionized grocery shopping by bringing groceries to your door in a little as an hour. The crux of the company is their shoppers, who shop in brick and mortar stores and bring the food to customers thousands of times per hour. Making these shoppers as efficient as possible is critical to the business. Hear how Instacart is applying deep learning to the shopping list to improve shopper efficiency, predicting the sequence that shoppers pick items in specific store locations – in some cases saving significant time in-store. Here Jeremy discusses the data collection, mobile technology and machine learning approaches Instacart is applying to enable on-demand grocery delivery.

View a selection of presentations from the 2017 Machine Intelligence Summit in San Francisco here, or contact Chloe cpang@re-work.co for video membership options.


Link to Full Article: Read Here

CREATING A MORE EFFICIENT SHOPPING EXPERIENCE WITH DEEP LEARNING

CREATING A MORE EFFICIENT SHOPPING EXPERIENCE WITH DEEP LEARNING

Retail is undergoing an artificial intelligence revolution. The latest advancements in deep learning algorithms are now impacting all corners of the industry, from stock optimization and smart warehousing, to search, recommendation systems and forecasting.

These significant advancements in AI, data science and deep learning are giving online shoppers and sales staff the tools they need for the most the efficient shopping experience possible. At Instacart, these technologies are allowing them to predict the sequence that customers pick items in specific store locations, in some cases saving their product pickers upwards of 10% of their time spent locating and gathering items in-store. This efficiency is extremely valuable in the competitive and continually evolving world of online shopping.

At the 2017 Machine Intelligence Summit in San Francisco, Jeremy Stanley, VP of Data Science at Instacart, shared expertise on how deep learning can be used to create more efficient online shopping, with insights into the data collection, mobile technology and machine learning approaches they are applying to enable on-demand grocery delivery. View his presentation with slides below.

Instacart has revolutionized grocery shopping by bringing groceries to your door in a little as an hour. The crux of the company is their shoppers, who shop in brick and mortar stores and bring the food to customers thousands of times per hour. Making these shoppers as efficient as possible is critical to the business. Hear how Instacart is applying deep learning to the shopping list to improve shopper efficiency, predicting the sequence that shoppers pick items in specific store locations – in some cases saving significant time in-store. Here Jeremy discusses the data collection, mobile technology and machine learning approaches Instacart is applying to enable on-demand grocery delivery.

View a selection of presentations from the 2017 Machine Intelligence Summit in San Francisco here, or contact Chloe cpang@re-work.co for video membership options.

BOOTSTRAPPING AN INTELLIGENT RECOMMENDER SYSTEM

BOOTSTRAPPING AN INTELLIGENT RECOMMENDER SYSTEM

In many different web services, machine learning is being used for recommendation systems that help users tackle information overload: there are simply too many movies, songs, and books for users to usefully browse through. Without such tools, some services are rapidly falling behind and losing customers.

Travel is a little bit different, as the world does not have millions of cities, but finding new, interesting places to travel to is still a challenge. Years ago, Skyscanner started it’s ‘everywhere’ search which allows users to find the cheapest places possible that they could travel to, leading to research showing that price is one of many factors that make a place attractive and interesting.

Neal Lathia, Senior Data Scientist at Skyscanner, will join us at the Machine Intelligence Summit in Amsterdam, to share how the company bootstrapped a destination recommender system using rich implicit data generated by millions of users, along with simple algorithmic approaches, and experiments that gauge how localised and personalised recommendation affects user engagement. I spoke to Neal ahead of the event to learn more.

Please tell us more about your work at Skyscanner.

As a Senior Data Scientist, my focus is on designing and building machine learning features for Skyscanner’s mobile app. Since joining, just under a year ago, the projects I’ve been working on have related to recommendation and search result ranking. However, the app creates a very rich ecosystem of data, and we have already identified a number of other opportunities ahead.

What do you feel are the leading factors enabling recent advancements in machine learning for recommendation systems?

Many of the near state-of-the-art algorithms for recommendation systems have been open sourced- which is always welcome news! The research field has also always been driven by open data challenges. Most importantly, the research community has always taken a multidisciplinary approach – not all recommender system challenges need to be solved with machine learning.

Which industries have the biggest potential to be impacted by advancements in recommendation systems?

As someone who has a background in recommender systems, it is difficult for me to try to envisage any industry without the lens of recommendation potential. There are so many facets of life where personalised information could be useful – from healthcare to travel and beyond.

What developments can we expect to see in machine intelligence in the travel industry in the next 5 years?

Many of the best known travel sites online have a distinct focus on price – helping users find the cheapest flight, hotel, or car (Skyscanner is no exception to this!). As these services gain greater smartphone traction, and data (e.g., flight statuses and prices) becomes available in real-time, the travel industry is going to become a ripe domain for machine intelligence applications.

Outside of your own field, what area of machine learning do you will see the most progress in the next 5 years?

There is no doubt that recent advances in neural networks have lead to wonderful results in the areas of reinforcement learning and machine vision – I expect that progress to continue to accelerate. I’m looking forward to interesting products that may arise from these areas of research.

Neal Lathia will be speaking at the Machine Intelligence Summit, taking place alongside the Machine Intelligence in Autonomous Vehicles Summit in Amsterdam on 28-29 June. Meet with and learn from leading experts about how AI will impact transport, manufacturing, healthcare, retail and more.
Other confirmed speakers include Roland Vollgraf, Research Lead, Zalando Research; Alexandros Karatzoglou, Scientific Director, Télefonica; Sven Behnke, Head of Autonomous Intelligent Systems Group, University of Bonn; Damian Borth, Director of the Deep Learning Competence Center, DFKI; Daniel Gebler, CTO, Picnic; and Adam Grzywaczewski, Deep Learning Solution Architect, NVIDIA. View more speakers and topics here.
Tickets are limited for this event. Register to attend now.

Opinions expressed in this interview may not represent the views of RE•WORK. As a result some opinions may even go against the views of RE•WORK but are posted in order to encourage debate and well-rounded knowledge sharing, and to allow alternate views to be presented to our community.

The Future of Investment in AI Could Be Decided By AI Itself

The Future of Investment in AI Could Be Decided By AI Itself

Many believe that artificial intelligence is set to reshape global economies and society, with AI expected to double economic growth. But what are the opportunities and challenges to investing in the current world of AI?

In 2016, the AI market was worth just $644 million, according to Tractica. This year that amount is due to almost double and continue to grow exponentially, with predictions showing it to reach $36.8 billion by 2025. With so many pioneering companies and world-leaders focusing their attention and funding on artificial intelligence fields such as deep learning and machine intelligence, the momentum for progress is well underway.

At the Machine Intelligence Summit on 28-29 June, Julius Rüssmann, Analyst at Earlybird Venture Capital, will share expertise on a panel exploring the challenges and opportunities of investing in AI. Julius believes investors themselves will be disrupted by the AI revolution. Could future investments in AI be decided by other artificially intelligent systems instead of humans? I spoke to him ahead of the summit to learn more.

What are in your opinion the most promising machine intelligence sectors to invest in?

Broadly defined, the service and manufacturing industries will probably benefit the most from (an increased level) of Machine Intelligence. By that I refer to the idea that large parts of the service industry, today still based on human work, can be heavily digitized, automatized and even improved through intelligent software applications. Especially, when considering the fact that the complexity inherent to each and every service inquiry increases, exponentially as data growth, machine intelligence is critical to ensure smoothly running system.

Besides primarily consumer focused service applications, the manufacturing industry will not remain the same. As machine intelligence develops, the sharp line between human-based work and robotics vanishes (think of collaborative robotics) until robotics will effectively overtake the bulk share of manufacturing processes and frees up billions of hours every day that have been had allocated to human work beforehand (problems that will arise).

One good example of Machine Intelligence that will redefine industries and humans alike is enriched or contextual computer vision. If it would be possible for software to contextually accurately understand video content, that would change a lot (autonomous driving, health care and so forth).

Besides industries and application fields, we deem Deep Reinforcement Learning as well as Neural Networks to be critical to facilitate further use-cases of Machine Intelligence.

What are the characteristics you are looking for in a startup prior to investing?

First and foremost, we look at the people behind the startup. Why is that? Because we think that complementary skills and solid commitment are in every successful company the kea driver. Starting a company, especially in the field of technology, always implies that there will be tough times and complex problems to be solved. In those situations it is almost irrelevant, how attractive you business model or the targeted market is. It is all about the team, to steer and pivot the company in the right direction (again). Besides outstanding people, we like to see early product (prototypes) to see credibility on execution, management and skills. In most cases that turn out to be successful you will see some sort of market adaption or commercial traction already early on, as customers and markets grave for such a solution and are open to use the new offering (think of solving a real problem.

How will venture capitalists be impacted by machine intelligence in the next 5 years?

Venture capitalists (VCs) will face a two-sided effect. First of all deal flow will significantly increase in the area of Machine Intelligence-based companies that are able to produce and deliver a solid value proposition to the market and are truly disruptive to specific industries. Today we still see a lot of evolutionary Machine Intelligence applications compared to revolutionary business models, many cases we see are rather a MI feature set, but not a standalone business case or company – this will change.

VCs will be threatened and potentially even disrupted themselves by MI. In essence, VC is also just a service industry (we service our portfolios and LP’s) and evidence clearly suggests that advanced MI will reach better (investment) decisions than humans do. However, the question remains how long this development will take; and yet, there is no clear evidence that MI will be capable of assessing or completely understanding humans. Considering, what has been stated in the beginning, namely that team are key, it’s not clear whether MI will necessarily reach better decision.

What are the dangers of no distinguishing between hype and reality in AI?

As explained above, AI/ML/MI are today quite advanced but not ultimately “ready” yet. This means that a lot of application fields (e.g. customer service industry) can clearly benefit from the introduction of smart algorithms (get more efficient, partially replace humans, better results, faster etc.), but they are not yet ripe for disrupting those industries effectively. So the danger is, from a VC’s or Founders perspective, to overestimate the capabilities of the algorithm, or to underestimate the importance of human-based decisions and verification. Effectively, there is lot of (technical) work still to be done and markets are only about to open up or be created for AI application. Time to market is critical for building up good investment cases.

Do VCs have a role in progressing the fields involved with AI?

VC’s, as with every other technology field, are responsible in finding and funding the leading teams/brains/companies in the respective field. This work is critical to contribute to the technology’s further development, to facilitate market adoption, to help identify viable business models and so forth (offering capital to outstanding entrepreneurs will improve the economy and the startup ecosystem in any ways). By finding and funding good technology companies in the AI field, VC’s also help to steer public attention to this area and to help create flagship project that then attract more brain-power and top-talent. As state before, it also in the responsibility of VC’s to be critical in their decision process also in order to prevent over-hype and bubble effects. It is sort of an educational responsibility that VC’s have for the tech ecosystem, the economy and the society.

Julius Rüssmann will be speaking at the Machine Intelligence Summit, taking place alongside the Machine Intelligence in Autonomous Vehicles Summit in Amsterdam on 28-29 June. Meet with and learn from leading experts about how AI will impact transport, manufacturing, healthcare, retail and more.
Other confirmed speakers include Roland Vollgraf, Research Lead, Zalando Research; Alexandros Karatzoglou, Scientific Director, Télefonica; Sven Behnke, Head of Autonomous Intelligent Systems Group, University of Bonn; Damian Borth, Director of the Deep Learning Competence Center, DFKI; Daniel Gebler, CTO, Picnic; and Adam Grzywaczewski, Deep Learning Solution Architect, NVIDIA. View more speakers and topics here.
Tickets are limited for this event. Register to attend now.

Opinions expressed in this interview may not represent the views of RE•WORK. As a result some opinions may even go against the views of RE•WORK but are posted in order to encourage debate and well-rounded knowledge sharing, and to allow alternate views to be presented to our community.

Human Computer Interfaces: Neural Lacing and Brain Uploads – The Future Or Our Demise

Ever since Elon Musk announced his acquisition of Neuralink in March 2017, the news has been awash with criticism and commentaries on human computer interfaces.

Perhaps the most widely shared of these is the (excruciatingly long) Wait But Why article that broke the Neuralink news. In the article, Tim Urban explains in minute detail how the technology will work. If you haven’t had the pleasure yet, you can read ‘Neuralink and the Brain’s Magical Future’ here.

The concept of human computer interfaces isn’t entirely new, though the Elon Musk news has driven it into the mainstream. Acclaimed futurist and transhumanist guru, Ray Kurzweil, has been championing human-machine symbiosis for much of his career, predicting that “the nonbiological portion of our intelligence will predominate” by the 2030s. He anticipates the full-blown Singularity by 2045. It is only fairly recently, however, that companies like Neuralink (and even Facebook) have started declaring that the technology may well be within our sights.

Presuming that this really is the case (Noam Chomsky reckons it’s impossible), we must begin to ask ourselves whether the merging of humans with technology is actually a good idea.

Clearly, humankind hasn’t been doing such a good job of things here on Earth recently. If we can become vastly more intelligent through human computer interfaces, perhaps we could do much better. If integrating ourselves with technology allows us to more accurately assess the consequences of our actions, then maybe a brighter future is ahead.

Why Human Computer Interfaces?

The reason that Elon Musk has decided to invest in neural lacing is linked to his concerns about the threat of artificial superintelligence – the Singularity that Kurzweil is convinced will be upon us in less than 20 years. This is a concern shared by many of the most prominent minds in science, including Stephen Hawking, who has also warned that the Singularity could be the greatest threat facing humankind.

Musk argues that human computer interfaces are our best chance of surviving the superintelligence explosion. In a kind of ‘if you can’t beat them, join them’ move, Musk sees our greatest chance lying in our ability to merge ourselves with those superintelligent machines. If we can do this, we remain capable of competing with machines on intelligence, retain more control over them, and thus improve our chances of survival by symbiosis.

If going bionic is an unnerving thought to you, then that’s really only natural. Such a vast alteration to how we live is bound to be a terrifying prospect, just as it was at the dawn of the first Industrial Revolution.

If the human computer interface is what will save us from our demise at the hands of the machine, we must necessarily overcome our squeamishness about letting machines into our bodies. The squeamishness itself can be argued to be a primitive concern, one that must be moved beyond if we are to survive.

Conscious Evolution

It is, certainly, a drastic solution to a problem we’re not altogether sure will even arrive. Nonetheless, we could consider it to be an unprecedented step: the point at which a species becomes so advanced that they can enact their own evolution.

Evolution, historically, has always been a natural process enacted over thousands and millions of years. When the environment fails to be sufficiently nurturing, changes begin to occur over generations to enable a species to better adapt to that environment. Those which cannot adapt die.

Technology, too, has been a slow evolutionary process, which arguably began from the time the first ape used the first rock to smash the first nut. A gradual process of evolution over millennia, leading us to the point at which we transcend biology, metamorphosing into our next form: a hybrid species of our own creation, imbued with far superior intelligence to our predecessors. A species thus capable of strengthening ourselves, of barricading death outside the door, moving on to colonise the universe: the ultimate lifeform.

Survival of the Richest?

The question remains, however, as to who will have access to the technology. Presumably, at least initially, it will be an expensive process and thus only available to the wealthy. So the rich become smarter, and an uneducated underclass of comparatively useless, stupid billions will emerge.

The only hope for the 99% is that this newly intelligent elite decide that it is better for the human race to be unanimously improved. Alternatively, it’s a simple case of survival of the fittest – those with the resources to survive. This, of course, is just one ethical consideration that needs to be raised. It’s a return to the old Doctor Strangelove question: who is fit enough for the nuclear bunker?

The question of how long we have to answer this and other questions posed by the issue is controversial. Whilst Kurzweil and others predict a very short window before artificial superintelligence arrives, others insist that we are nowhere near artificially superintelligent machines nor functional human computer interfaces. Whilst the lack of a definitive answer may lead many to dismiss the issue outright, the smarter decision, of course, is to prepare ourselves so that we are ready if and when the time comes.


Link to Full Article: Read Here

TECHXLR8 ANNOUNCES LONDON TECH WEEK’S HEADLINE SPEAKERS

Curated by KNect365, TechXLR8 brings together 8 leading technology events at London’s ExCeL, June 13-15, 2017.
Forming part of London Tech Week, TechXLR8 will play a central part in a week-long festival of live technology events taking place across the UK’s capital. It celebrates and cultivates London as a global powerhouse of tech innovation by connecting the entire ecosystem both within London and beyond.

“We are delighted to be hosting a group of industry leading speakers at TechXLR8. The London Tech Week Headline Stage will showcase the absolute pinnacle of technology innovation. These men and women are shaping the ways we will interact with technology in the future, and we are excited to share their messages with London, and the world,” says Carolyn Dawson, Events Director & Managing Director of KNect365, an Informa Plc business.

TechXLR8 and London Tech Week are pleased to announce the LTW Headline Stage speakers for 2017.

Bibop G Gresta, COO and Chairman, Hyperloop Technologies,
Marc Allera, CEO, EE
David Hanson, Hanson Robotics & Sophia the Robot
Janet Coyle, Principal Advisory for Growth, London & Partners
Emma Sinclair MBE, Co-Founder, Enterprise Jungle, Columnist, The Telegraph
Tamara Lohan, Founder & CTO, Mr & Mrs Smith
Stephen Kelly, CEO, Sage
Jodi Goldstein, Managing Director, Harvard Innovation Labs
Fiona Murray CBE, Associate Dean for Innovation, MIT Sloan School of Management; Co-Director, MIT Innovation Initiative
Robert Thomson, CEO, News Corp.
Richard Browning, Gravity
Marc Speichert, Global Chief Digital Officer, GSK
A further group of headline speakers are yet to be announced.

Press opportunities will be available on site for a selection of the headline speakers, if you would like to book interview time, please contact Rhian Wilkinson at Rhian.wilkinson@KNect365.com.

Interviews with TechXLR8 speakers from across the event spectrum are available for syndication from the links below:

What is TechXLR8?
Taking place 13-15 June in London, TechXLR8 incorporates 8 co-located events focusing on the cutting-edge technologies transforming industries and enterprises: Internet of Things, 5G, Virtual Reality & Augmented Reality, Connected Cars & Autonomous Vehicles, Cloud & DevOps, Artificial Intelligence & Machine Learning, and Apps.

Experience a show like no other with one shared exhibition, 20 tracks of content, 8 live demo zones, 40+ hours of networking, an awards ceremony and more. TechXLR8 is set to welcome 15,000+ attendees from over 8,000 companies over the three conference days.

Free visitor tickets for TechXLR8 include access to 300+ exhibitors and 50+ hours of content featuring over 150 industry leading speakers including speakers from NASA, BarclaysHSBCBTFacebookBPBBCUBSRalph Lauren and Lebara. What’s more a free visitor ticket also provides access to 8 demo zones on the latest emerging tech in 5G, Smart Cities, Robotics, Drones and more.

Watch the TechXLR8 launch video here: https://goo.gl/TMtRFn

Access to the London Tech Week Headline Stage is included in the TechXLR8 Free Visitor Ticket, registration is open now.

Free visitor tickets for TechXLR8 are available here: https://goo.gl/YWLoMQ

About London Tech Week, 12-16 June
London Tech Week is a festival of events, taking place across the city and representing the entire technology ecosystem.

No other festival of live events brings together as many domestic and international tech specialists and enthusiasts to London for such a variety of networking, social, learning and business opportunities.
Since its launch in 2014 London Tech Week has included more than 700 events and has welcomed delegations from around the world.

London Tech Week 2017 is organised by founding partners, KNect365, London & Partners and Tech London Advocates, with support from strategic partners Tech City UK, ExCeL London, DIT and techUK.
More information on whats happening during the week can be found at https://londontechweek.com/

Speakers Wanted

Many of the meetup and conference events are looking for guest speakers to present at one of their meetings. We frequently get asked if we know of anyone that is available to speak at events.

Speakers can be Authors, Academics or Professionals working on Artificial Intelligence. These meetings typically allow the opportunity for some self promotion or sales pitch.

If you would like to be added onto our list of potential speakers, please send us a message with some details of the locations and topics you can cover.

Speakers Contact Us

M.I.E. SUMMIT BERLIN 2017 – 20th June

The world’s first open-space Machine Intelligence summit, which will be held on the 20th of June 2017.

This event will give you the opportunity to learn, discuss and network with your peers in the MI field. Back dropped in one of Berlin’s most vibrant and artistic locations, break free from traditional conference rooms and share a drink in a typical Berliner Biergarten.

The M.I.E Summit Berlin 2017 will provide you with two in-depth event tracks (keynotes, workshops, and panels) as well as over 20 leading speakers and unparalleled networking opportunities.

The following topics will make this event one of the most inspiring, entertaining and thought-provoking this year:

  • What exactly does AI mean for all industries, from medicine to cars, from cognitive to neural networks?
  • Can machines really outperform humans? What if AI systems become better than humans at all cognitive tasks?
  • Should you worry whether your job is going to be replaced by robots? If yes, what can you do about it?
  • You work on innovation and are eager to find out how AI could apply to your business?
  • How can we benefit from the great advancements brought about by AI while taking into account ethical and economical considerations?
  • Is investing in AI startups a good idea? What’s behind the hype?

 

We are pleased to offer a 30% discount code for this event of using code “miepartners

https://www.eventbrite.com/e/mie-summit-berlin-2017-can-machine-ai-outperform-human-tickets-33207267832

home of Artificial Intelligence information

Resource Directory, News Stories, Videos, Twitter & Forum Streams, Spotlight, Awards, Showcase and Magazine

Pin It on Pinterest

Share This

Join Our Newsletter

Sign up to our mailing list to receive the latest news and updates about homeAI.info and the Informed.AI Network of AI related websites which includes Events.AI, Neurons.AI, Awards.AI, and Vocation.AI

You have Successfully Subscribed!