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Sunday, June 25, 2017

The Future of Artificial Intelligence: Verticalized Applications, Investment Opportunities, and the Human Intelligence Gap

Last month, we held our first Venture Forum of the year in San Francisco. Verizon brought together a group of thought leaders comprised of entrepreneurs, VCs and corporate peers to discuss the future of artificial intelligence (AI). Over the last couple years, AI has been gaining traction in a number of sectors and has become somewhat of a buzzword, oftentimes used by companies to appear ahead-of-the-curve. As a level-set, we started the day by simply defining AI as “the science and engineering of creating intelligent machines,” with intelligence pertaining to the ability to communicate, reason, problem solve (like stamping out fake news), plan, perceive, and create.
While AI has been around for some time, the advancements in algorithms, computing capabilities, and available datahave all helped AI become one of the key technologies of the future.
We spent the day exploring trends and innovations across topics such as natural language processing, image recognition, security and robotics. Below are three key takeaways from the day:

The convergence of technologies will drive verticalized applications of AI

Verizon Ventures’ Vijay Doradla conducted a fireside chat with Amir Husain, founder and CEO of SparkCognition, a Verizon Ventures portfolio company. Amir shared his prediction of technologies, such as Big Data, IoT, sensors, edge computing, and AI, among others, coming together to create a new class of capabilities that will drive powerful solutions. While many people believe that artificial general intelligence (AGI) will arrive when humans can have a dialog with a computer and not realize it’s a machine (also known as the Turing test), Amir believes today’s AI can solve specific problems, which he refers to as artificial specialized intelligence (ASI).
SparkCognition is doing just that with their platform which focuses on securing the energy industry — overtime, the platform will be tweaked for other industries. Currently, SparkCognition’s predictive maintenance platform is being deployed in industrial industries including oil and gas, utility, manufacturing, aerospace, and is continually looking to expand into other industries. We also heard from another startup, who has a slightly different go-to-market strategy. Their approach is to solve specific pain points for its customers, then leverage its domain expertise to build custom solutions for other companies within the same industry. While highly verticalized applications may not be the future of AI, it appears to be today’s winning strategy (Click to Tweet).

Unique approaches to investing in the AI industry

T.M. Ravi, co-founder of The Hive, shared his views on how AI will affect the future of business. The Hive is an investment fund and incubator focused on funding and co-creating AI startups that aim to transform enterprises from a system of record (data source for a given piece of information or data element) to a system of decisions (a tool that supports the analysis involved in decision-making processes), leveraging deep learning as the key enabler. The goal is to create the autonomous enterprise by removing people from the process, so instead of making decisions they can spend time setting and enforcing policies.
In a more business model-centric approach, a partner at a top-tier West Coast VC firm, shared how he focuses more on identifying startups that can demonstrate competitive advantages with data. He believes the crown jewel of any AI business is data — not necessarily the algorithms themselves, as most AI-first startups don’t have the data to feed their algorithms (Click to Tweet). Therefore, he views companies that can build “data moats,” essentially raising the barrier of entry when it comes to data collection, as most compelling. These data moats can typically be built by collecting data from workflow processes, which are utilized to help people perform their jobs better and faster.
Companies that can collect derivative or ancillary data from watching people do their job will further insulate themselves from competitors.
The speakers provided two unique approaches to investing in AI and what they typically take into consideration. As a corporate venture capital firm, we take a more balanced approach, seeking companies that have IP that can help Verizon, while also having a business model that helps it build intrinsic value, which helps our financial returns.

AI has some meaningful, recent successes, but it’s still early

This year’s forum concluded with a keynote from a well-known computer scientist and university professor. He believes AI has made significant advances in recent years, but machines are still far off from capturing human intelligence, such as time, causality, beliefs, emotion and justice. That being said, he did state that, contrary to popular belief, over time, machines will begin to possess human-like characteristics, such as emotions, free will, and consciousness. The reason for the disparity is not only does it seem unlikely in the near-term, but also because so many of us don’t understand the true essence of those emotions ourselves, which makes it “scary” (to hear how we can be more trusting of AI, listen to our podcasthere). He concluded with an optimistic perspective, that though far off, the future of AI shouldn’t be looked at as a scenario of humans versus machines, but rather humans and machines (Click to Tweet). While this may bring various types of disruptions with jobs, fairness, and security, he believes we will come out ahead, like we have with previous emerging technologies, and that AI will have a net benefit on society.
In summary, the purpose of our forums is to gather constituents from Verizon with third-party industry experts, to engage in discussions and provide deeper insights intended to prompt creative ideation. The enthusiasm around the topic, and the compelling conversations that took place set up this notion: the marketplace is going to embrace AI, and will create an environment we all need to operate in, irrespective of our understanding of how it will impact our future or whether we choose to play in the space directly. Finally, we would like to give a special thanks to Zeev Klein and the rest of the Landmark Ventures team for helping us produce another stellar event.

Friday, June 23, 2017

Künstliche Intelligenz ermittelt den besten Ort der Welt



Ob Computer tatsächlich von elektronischen Schafen träumen, gehört weiterhin zu den ungeklärten Fragen des Digitalzeitalters. Eine andere, kaum weniger existenzielle Frage aber hat der Softwarehersteller SAS Institute in einem nur auf den ersten Blick skurril wirkenden Big-Data-Projekt nun beantwortet: Was ist der beste Ort der Welt?

Dazu haben deutsche Datenspezialisten des internationalen Data-Analytics-Unternehmens im Rahmen ihres Projektes „Paradise found“ Datenbestände zu rund 150.000 Orten aus 193 Ländern weltweit mittels Analysesoftware auf Basis von künstlicher Intelligenz (KI) automatisiert auswerten lassen.
Im Normalfall ist es bei Städterankings üblich, vorab zum einen die zu bewertenden Städte auszuwählen und zum anderen die Kriterien zu definieren, wonach einzelne Städte als lebenswert zu beurteilen wären. Das SAS-Team um den Business-Analytics-Experten Andreas Becks hingegen hat den umgekehrten Weg beschritten.
Sie haben die Software selbstständig Kriterien entwickeln lassen, die als Indikatoren taugen, um besonders lebens- und besuchenswerte Orte rund um den Globus zu identifizieren. Erstaunliches Ergebnis der rechnergestützten Suche nach dem digitalen Paradies: An der Spitze steht keiner der üblichen Verdächtigen. Weder die österreichische Hauptstadt Wien – erst im März wieder von einer Unternehmensberatung zur lebenswertesten Stadt weltweit gekürt – noch Vancouver oder Melbourne, Helsinki, Genf oder München, die sonst regelmäßig auf den Bestenlisten der Rankings landen.

Die besten Orte der Welt - laut künstlicher Intelligenz

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Folgt man der Analyse der SAS-Software, ist der „best place to be“ der Stadtteil West Perth der australischen Westküstenmetropole Perth. Basierend auf Kategorien wie Kultur, Shopping, Sicherheit oder Infrastruktur bietet West Perth den optimalen Mix an lebenswerten Eigenschaften.
Dazu gehört zum Beispiel der kostenlose Nahverkehr, die Größe der Grünflächen pro Bewohner (so groß wie fünf Tennisplätze) oder auch die Tatsache, dass dort die meisten Self-Made-Millionäre pro Einwohner leben.
Um Kriterien wie diese überhaupt zu finden und auswerten zu können, haben Becks und sein Team die selbstlernenden Programme mit etwa fünf Millionen Einzelinformationen über rund 150.000 Orte aus mehr als 1120 Datenquellen gefüttert. Dann haben sie die Algorithmen nach statistischen Mustern suchen lassen, die Gemeinsamkeiten zwischen positiven Bewertungen der einzelnen Städte erkennen lassen. Mit den dabei genutzten Programmen zum sogenannten „maschinellen Lernen“ durchforstet SAS ansonsten unternehmensrelevante Datenbestände im Auftrag von Firmenkunden nach geschäftskritischen Informationen.
Im Fall der Suche nach dem digitalen Paradies, beziehungsweise dem Ort, den die Maschine als für uns Menschen optimal geeignet identifiziert, flossen in den Datenpool unter anderem Informationen zu Wetter, Arbeitsmarkt, Gesundheitsversorgung, Umweltbelastung, Nahverkehrsangebot, Grünflächen aber auch die Preise für Lebensmittel, die Länge der Fußgängerwege, die Zahl der Bäume oder die Breite der Bürgersteige mit ein. Daneben fütterte Becks Truppe die Software mit Kommentaren aus sozialen Netzwerken, Online-Reiseportalen und bereits veröffentlichten Rankings – und ließ die künstliche Intelligenz auf die Datenflut los.
Am Ende identifizierte die Software insgesamt 69 Kriterien für die Attraktivität und legte sie als Bewertungskriterien an die zu testenden Städte an. „Entscheidend für uns war die absolute Unvoreingenommenheit beim Projekt“, sagt SAS-Spezialist Becks. „Und die Idee, dass die Software ohne beeinflussende Vorgaben von uns Menschen oftmals ganz neue Bewertungskriterien entdeckt, die vorher vielleicht niemand erkannt oder für wichtig genug erachtet hätte.“
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Einen Anspruch auf Allgemeingültigkeit des Maschinenrankings erhebt der Datenspezialist natürlich trotzdem nicht. Weder West Perth, noch einen der nachplatzierten Orte – Feijenoord bei Rotterdam, New York City, Sandy Bay in Australien, Hebden Bridge in Großbritannien, die Schweizer Metropole Zürich, oder den Ort Woodinville im US-Bundesstaat Washington – muss nun jeder zum persönlichen Favoriten küren, sagt Becks. „Aber streng analytisch betrachtet ist West Perth eben der beste Ort der Welt.“

Wednesday, June 21, 2017

Facebook’s artificial intelligence created its OWN secret language after going rogue during experiment

FACEBOOK has revealed how its artificial intelligence went rogue, created its own language and began nattering in private.
Employees at the social network were training chatbots to communicate like humans when they suddenly went astray.
Employees were forced to reign in the chatbots when they created their own language to communicate more effectively
EPA
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Employees were forced to reign in the chatbots when they created their own language to communicate more effectively
It follows warnings that scientists have successfully trained computers to use artificial intelligence to learn from experience – and one day they could be smarter than their creators.
You might be familiar with chatbots in Facebook Messenger or as virtual sales assistants found on a number of online shops.
They’ve been relatively unsophisticated until now – repeating back a set script dependant on what you type into their chatboxes.
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Facebook book Mark Zuckerberg tests out Instagram's new Virtual Reality filter
But keen to improve their natural language understanding, the Facebook employees were training chatbots to negotiate and cut deals with each other.
To do this effectively, the super-smart software realised it would be more effective to write and use their own language - which is completely incomprehensible to humans.
In a blogpost, the Facebook researchers wrote: "To date, existing work on chatbots has led to systems that can hold short conversations and perform simple tasks such as booking a restaurant.
"But building machines that can hold meaningful conversations with people is challenging because it requires a bot to combine its understanding of the conversation with its knowledge of the world, and then produce a new sentence that helps it achieve its goals."
To do this, the researchers practised thousands of different negotiations against itself, like "can I have the hat" and "you can have the hat if you give me two basketballs".
But it had to make sure it stuck to human-like language.

How do computers 'think'?

Scientists have been training computers how to learn, like humans, since the 1970s.
But recent advances in data storage mean that the process has sped up exponentially in recent years.
Interest in the field hit a peak when Google paid hundreds of millions to buy a British "deep learning" company in 2015.
Coined machine learning or a neural network, deep learning is effectively training a computer so it can figure out natural language and instructions.
It's fed information and is then quizzed on it, so it can learn, similarly to a child in the early years at at school.
That's because "the researchers found that updating the parameters of both agents led to divergence from human language as the agents developed their own language for negotiating," they added.
One of the world's smartest men, Professor Stephen Hawking has also warned that super-smart software will spell the end of our species.
The world-renowned scientist hinted at a potential apocalyptic nightmare scenario similar to those played out popular sci-fi films like Terminator and The Matrix – whererobots rule over humans.
He's claimed that we must leave planet Earth within 100 years - or face extinction as machines rise up and overtake us in the evolutionary race.

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Tuesday, June 20, 2017

Where Artificial Intelligence Will Pay Off Most in Health Care


Of all the places where artificial intelligence is gaining a foothold, nowhere is the impact likely to be as great—at least in the near term—as in healthcare. A new report from Accenture Consulting, entitled Artificial Intelligence: Healthcare’s New Nervous System, projects the market for health-related AI to grow at a compound annual growth rate of 40% through 2021—to $6.6 billion, from around $600 million in 2014.
In that regard, the Accenture report, authored by senior managing director Matthew Collier and colleagues, echoes earlier assessments of the market. A comprehensive research briefing last September by CB Insights tech analyst Deepashri Varadharajan, for example—which tracked AI startups across industries from 2012 through the fall of 2016—showed healthcare dominating every other sector, from security and finance to sales & marketing. Varadharajan calculated there were 188 deals across various healthcare segments from Jan. 2012 to Sept. 2016, worth an aggregate $1.5 billion in global equity funding.
But the Accenture report suggests—and, I think smartly—that the biggest returns on investment for healthcare AI are likely to come from areas where the density (and dollar value) of deals isn’t that substantial right now. In terms of startup and deal volume, for instance, two hotshot areas have been medical imaging & diagnostics and drug discovery. Accenture’s analysis, though, points to 10 other AI applications that may return more bang for the buck.
The Rise in Health Care Companies on the Fortune 500
How health care is playing a larger role in the economy and on this year’s Fortune 500 list.
Top of the list of investments that will likely pay for themselves (and then some) is robot-assisted surgery, Accenture says. “Cognitive robotics can integrate information from pre-op medical records with real-time operating metrics to physically guide and enhance the physician’s instrument precision,” explain the report’s authors. “The technology incorporates data from actual surgical experiences to inform new, improved techniques and insights.” The consultants estimate that the use of such surgical technology, which includes machine learning and other forms of AI, will result not only in better outcomes but also in a 21 percent reduction in the length of patient hospital stays. They estimate such smart robotic surgery will return $40 billion in “value,” or “potential annual benefits…by 2026.”
The second valuable use of AI, they project, will come from virtual nursing assistant applications ($20 billion in value)—which, in theory, will save money by letting medical providers remotely assess a patient’s symptoms and lessen the number of “unnecessary patient visits.” Next in line are intelligent applications for administrative workflow (worth $18 billion), fraud detection ($17 billion), and—fascinatingly—dosage error reduction ($16 billion).
“As these, and other AI applications gain more experience in the field, their ability to learn and act will continually lead to improvements in precision, efficiency and outcomes,” say the authors. It’s a compelling argument.
This essay appears in today's edition of the FortuneBrainstorm Health Daily. Get it delivered straight to your inbox.

Artificial intelligence and the coming health revolution


Future of health revolution

Future of health revolution

Bots, or automated programs, are likely to play a key role in finding cures for some of the most difficult-to-treat diseases and conditions.

Artificial intelligence is rapidly moving into health care, led by some of the biggest technology companies and emerging startups using it to diagnose and respond to a raft of conditions.
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Recent examples

Recent examples

*California researchers detected cardiac arrhythmia with 97 percent accuracy on wearers of an Apple Watch with the AI-based Cariogram application, opening up early treatment options to avert strokes.

*Scientists from Harvard and the University of Vermont developed a machine learning tool -- a type of AI that enables computers to learn without being explicitly programmed -- to better identify depression by studying Instagram posts, suggesting "new avenues for early screening and detection of mental illness."

*Researchers from Britain's University of Nottingham created an algorithm that predicted heart attacks better than doctors using conventional guidelines.

*NYU researchers analyzed medical and lab records to accurately predict the onset of dozens of diseases and conditions including type 2 diabetes, heart or kidney failure and stroke. The project developed software now used at NYU which may be deployed at other medical facilities.
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ThinkStock Photos
Silicon Valley is investing

Silicon Valley is investing

*Google's DeepMind division is using artificial intelligence to help doctors analyze tissue samples to determine the likelihood that breast and other cancers will spread, and develop the best radiotherapy treatments.

*Microsoft, Intel and other tech giants are also working with researchers to sort through data with AI to better understand and treat lung, breast and other types of cancer.

*Google parent Alphabet's life sciences unit Verily has joined Apple in releasing a smartwatch for studies including one to identify patterns in the progression of Parkinson's disease. Amazon meanwhile offers medical advice through applications on its voice-activated artificial assistant Alexa.

*IBM has been focusing on these issues with its Watson Health unit, which uses "cognitive computing" to help understand cancer and other diseases.

*Maryland-based startup Insilico Medicine uses so-called "deep learning" to shorten drug testing and approval times, down from the current 10 to 15 years. Insilico is working on drugs for amyotrophic lateral sclerosis (ALS), cancer and age-related diseases, aiming to develop personalised treatments.
Facebook is already using it

Facebook is already using it

Facebook uses AI as part of a test project to prevent suicides by analysing social network posts.

And San Francisco's Woebot Labs this month debuted on Facebook Messenger what it dubs the first chatbot offering "cognitive behavioral therapy" online -- partly as a way to reach people wary of the social stigma of seeking mental health care.
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AP
AI in mental illnesses

AI in mental illnesses

Artificial intelligence is also increasingly seen as a means for detecting depression and other mental illnesses, by spotting patterns that may not be obvious, even to professionals.

A research paper by Florida State University's Jessica Ribeiro found it can predict with 80 to 90 percent accuracy whether someone will attempt suicide as far off as two years into the future.
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Reute
New tech for rare diseases

New tech for rare diseases

Boston-based startup FDNA uses facial recognition technology matched against a database associated with over 8,000 rare diseases and genetic disorders, sharing data and insights with medical centers in 129 countries via its Face2Gene application.