Daily Archives: July 2, 2017

China will see the greatest economic gains from artificial intelligence (AI) by 2030 as the technology accelerates global GDP growth by increasing productivity and boosting consumption, says PwC in a new research report released Tuesday.

Dubbed the fourth industrial revolution, AI technologies are expected to boost global GDP by a further 14 per cent by 2030 the equivalent of an additional US$15.7 trillion and China, as the worlds second largest economy, will see an estimated 26 per cent boost to GDP by that time, the PwC report said.

Launched at the World Economic Forums annual June meeting in northeast Chinas Dalian city, often known as the Summer Davos, the report said labour productivity improvements would account for over half of the US$15.7 trillion in economic gains from AI between 2016 and 2030 more than the current output of China and India combined while increased consumer demand resulting from AI-enabled product enhancements will account for the rest.

The analysis demonstrates how big a game changer AI is likely to be transforming our lives as individuals, enterprises and as a society, said Anand Rao, global leader of artificial intelligence at PwC.

The future is here: China sounds a clarion call on AI funding, policies to surpass US

The technology behind an array of advanced applications, from facial recognition to self-driving vehicles, is the centre of attention for almost every tech company in China as they bet big on AI to gain a competitive edge before it begins to have a more profound impact on peoples lives.

Since the start of this year, Chinese internet heavyweights Baidu, Tencent Holdings and Alibaba Group have been competing harder than ever to lure top AI talent from Silicon Valley in order to accelerate their own AI development. Alibaba owns the South China Morning Post.

PwC predicts that North America will experience productivity gains earlier than China due to its first mover advantage in AI but China is expected to pull ahead of the United States in terms of AI productivity gains within 10 years after it catches up to the technology.

According to the PwC research, AI is projected to boost Chinas GDP by 26 per cent by 2030, while for North America the number is 14.5 per cent. For developing countries in Latin America and Africa, the expected GDP gain will only be about 6 per cent due to the much lower rates of AI technology adoption.

China has already made great leaps in the development of AI and our research shows that [AI] has the potential to be a powerful remedy for slowing growth, said Chuan Neo Chong, chairwoman of Greater China operations for global consultancy Accenture.

Artificial intelligence could put as many as 50m Asian jobs at risk over next 15-20 years: UBS study

In separate research done by Accenture, AI is expected to accelerate Chinas annual growth rate from 6.3 per cent to 7.9 per cent by 2035. The Accenture research, published on Monday, shows that AI could boost Chinas gross value added (GVA) by US$7.11 trillion by 2035 and has the potential to boost Chinas labour productivity by 27 per cent by the same year.

Minimising the economic imbalances brought about by AI will be an important challenge, said Lee Kai-fu, the former Greater China president of Google and founder of venture capital firm Sinovation Ventures.

Those developing countries which will experience rapid population growth in coming decades are expected to be hardest hit by AI in terms job losses, he added.

Most of the wealth created by AI will go into the US and China because of their big pool of talent and [high levels of data generation], as well as the size of their markets, said Li, who is one of the most prominent advocates of AI in China.

See the original post:

China is betting big on AI - and here's why it's going to pay off - South China Morning Post

LONDON The world's leading drug companies are turning to artificial intelligence to improve the hit-and-miss business of finding new medicines, with GlaxoSmithKline unveiling a new $43 million deal in the field on Sunday.

Other pharmaceutical giants including Merck & Co, Johnson & Johnson and Sanofi are also exploring the potential of artificial intelligence (AI) to help streamline the drug discovery process.

The aim is to harness modern supercomputers and machine learning systems to predict how molecules will behave and how likely they are to make a useful drug, thereby saving time and money on unnecessary tests.

AI systems already play a central role in other high-tech areas such as the development of driverless cars and facial recognition software.

"Many large pharma companies are starting to realize the potential of this approach and how it can help improve efficiencies," said Andrew Hopkins, chief executive of privately owned Exscientia, which announced the new tie-up with GSK.

Hopkins, who used to work at Pfizer, said Exscientia's AI system could deliver drug candidates in roughly one-quarter of the time and at one-quarter of the cost of traditional approaches.

The Scotland-based company, which also signed a deal with Sanofi in May, is one of a growing number of start-ups on both sides of the Atlantic that are applying AI to drug research. Others include U.S. firms Berg, Numerate, twoXAR and Atomwise, as well as Britain's BenevolentAI.

"In pharma's eyes these companies are essentially digital biotechs that they can strike partnerships with and which help feed the pipeline," said Nooman Haque, head of life sciences at Silicon Valley Bank in London.

"If this technology really proves itself, you may start to see M&A with pharma, and closer integration of these AI engines into pharma R&D."

STILL TO BE PROVEN

It is not the first time drugmakers have turned to high-tech solutions to boost R&D productivity.

The introduction of "high throughput screening", using robots to rapidly test millions of compounds, generated mountains of leads in the early 2000s but notably failed to solve inefficiencies in the research process.

When it comes to AI, big pharma is treading cautiously, in the knowledge that the technology has yet to demonstrate it can successfully bring a new molecule from computer screen to lab to clinic and finally to market.

"It's still to be proven, but we definitely think we should do the experiment," said John Baldoni, GSK's head of platform technology and science.

Baldoni is also ramping up in-house AI investment at the drugmaker by hiring some unexpected staff with appropriate computing and data handling experience - including astrophysicists.

His goal is to reduce the time it takes from identifying a target for disease intervention to finding a molecule that acts against it from an average 5.5 years today to just one year in future.

"That is a stretch. But as we've learnt more about what modern supercomputers can do, we've gained more confidence," Baldoni told Reuters. "We have an obligation to reduce the cost of drugs and reduce the time it takes to get medicines to patients."

Earlier this year GSK also entered a collaboration with the U.S. Department of Energy and National Cancer Institute to accelerate pre-clinical drug development through use of advanced computational technologies.

The new deal with Exscientia will allow GSK to search for drug candidates for up to 10 disease-related targets. GSK will provide research funding and make payments of 33 million pounds ($43 million), if pre-clinical milestones are met.

(Reporting by Ben Hirschler; Editing by Adrian Croft/Keith Weir)

JAKARTA Indonesia has set minimum and maximum tariffs for online car-hailing services, aiming to ensure comparable pricing with conventional transport providers whose drivers have complained about being undercut by their newer competitors.

TORONTO The U.S government warned industrial firms this week about a hacking campaign targeting the nuclear and energy sectors, the latest event to highlight the power industry's vulnerability to cyber attacks.

Read more here:

Big pharma turns to AI to speed drug discovery, GSK signs deal - Reuters

Dr. Karim Galil was tired. He was tired of losing patients to cancer. He was tired of messy medical records. And he was tired of trying to stay on top of the avalanche of clinical trials touting one solution or another. Losing both patience and too many patients, Galil decided to create an organized and artificially intelligent system to match those under his care with thebest diagnostic and treatment methods available.

He called his new system Mendel.ai after Gregor Mendel, the father of modern genetics science, and has just raised $2 million in seed funding from DCM Ventures, Bootstrap Labs and Launch Capitalto get the project off the ground.

Mendel.ai is similar in many ways to the U.K.-based BenevolentBio, which is focused on skimming through scientific papers to find the latest in cutting-edge medical research. But rather than using keyword data, Mendal.ai uses analgorithm that understands the unstructured, natural language content within medical documents pulled from clinicaltrials.gov,and then compares it to a patients medical record. The search process returns a fully personalized match and evaluates the patients eligibility for each suggested treatment within minutes, according to Galil.

The startup could prove useful for doctors whoincreasingly find it difficult to keep up on the exhaustive amount of clinical data.

Patients are also overwhelmed at the prospect of combing through mountains of clinical trial research. A lung cancer patient, for example, might find 500 potential trials on clinicaltrials.gov, each of which has a unique, exhaustive list of eligibility criteria that must be read and assessed, says Galil. As this pool of trials changes each week, it is humanly impossible to keep track of all good matches.

Mendel.ai seeks to reduce the time it takes and thus save more lives. The company is now integrating with the Comprehensive Blood & Cancer Center (CBCC) in Bakersfield, Calif, which will allow the centers doctors to quickly match their patients with available clinical trials in a matter of minutes, according to Galil.

The plan going forward is to workwith hospitals and cancer genomics companies like the CBCC to improve Mendel.ai and introduce the system. A more immediate goal, Galil says, would be challenging IBMs Watson against his system to see which one can match up the patients better.

This is the difference between someone dying and someone living. Its not a joke, Galil told TechCrunch.

Read the original:

Mendel.ai nabs $2 million to match cancer patients with the latest ... - TechCrunch

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AI Will Make Forging Anything Entirely Too Easy - WIRED

One of the hottest areas for VC investment at the moment is AI/machine learning that includes artificial intelligence algorithms, related machine learning systems, neural networks, and back-end processing to produce insightful and self-learning applications. As Nvidias CEO recently said:

Software may be eating the world, but AI is going to eat software

VC investment in AI has risen from $3.2 billion in 2014 to $9.5 billion for the first five months of 2017 annualized, with the number of funding rounds nearly doubling since 2015 to over 1,200 on an annualized basis so far this year. No wonder Frost & Sullivan calls AI the hottest investment trend of 2017:

Above: Source: Pitchbook

Investors piling into a space aim for multiple exits worth hundreds of millions of dollars. However, the pattern of AI exits is the opposite. Most successfully-exited AI companies sell for below $50 million after raising only a small amount of money. This works well for founders and small angel backers but not for VCs looking for exits well over $100 million.

Of 70 AI M&A deals since 2012, 75 percent sold below $50 million. These deals are often acqui-hires companies acquired for talent not business performance. The number of $200 million+ deals barely registers.

Above: Source: Pitchbook

The typical journey goes like this: A small team comes together around 1-2 individuals, they forge real advances on key use cases (voice recognition, visual/video tracking, fraud detection, retail consumer behavior, etc.), sign a handful of prominent customers, raise less than $10 million (often less than $5 million), then attract the attention of a major buyer looking to solve that problem set. These kinds of AI companies are often valued as an amount paid per engineer rather than on performance (revenue, growth, profits); the average price per employee is around $2.5 million:

Above: Source: Pitchbook

The other issue for VCs is that AI companies dont generally need to raise much money, even if they are valued far above $100 million. Argo, valued at $1 billion for a majority stake by Ford, was 20 people when bought. Our research from PitchBook shows the 10 most valuable AI M&A targets raised on average only $15-25 million; there was only room for 1-2 VC investors in each deal:

Sure there are larger AI companies still growing, such as Palantir, valued at $10 billion having raised over $500 million. But a few isolated cases of $1 billion+ unicorns created using significant VC money is hardly fertile ground for 1,200 VC investment rounds. The reality is, AI just isnt as rich a segment for VCs as investment activity suggests.

Once several VCs invest, an AI company can no longer entertain a $50-100 million M&A offer and must scale its team and product suite to ramp to a much higher valuation years further out; otherwise VCs cannot get the return they require. Heres why we think this is counterproductive:

For many AI founders, the best approach is raising little money, demonstrating they can solve hard problems, and waiting for the M&A phone to ring.

For VCs the best approach is often to look elsewhere.

Victor Basta is founder of Magister Advisors, a specialist bank focused on M&A exits and larger financing rounds.

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For AI startups, more funding is often not the answer - VentureBeat

Of all the fields in the chatbot-crazed world, customer service is one of the prime targets for automation. virtual customer agents (customer service-focused bots, or VCAs) are intelligent systems able to understand what users ask via chat and to provide them with adequate answers. In the context of this article, when we talk about VCAs we mean systems that are able to understand natural language and texting and do not just operate in a rules-based multiple-choice environment. In short, these VCAs compete directly with humans to resolve customer service issues.

The current reality of chatbots nicely counterbalances all the hype that AI is getting and offers guidance as to where development is needed. Here are the key learnings weve gleaned from deploying VCAs that autonomously answer questions and having attended major customer service automation and chatbot summits:

Ideally, you can train the VCA with thousands of questions (complete with misspellings, grammatical errors, and pidgin dialects) from actual users of the product/service. But the reality is that most companies do not have existing chat history data readily available for training. In that case, the options are to artificially generate thousands of different questions or to deal with the reality of not having much input data and hope to gather it when the VCA goes live. Neither solution is ideal, and even if companies have a chat log history, it is generally unlabeled. This means the questions in the chat logs are not paired withintents. Fully manual pairing of thousands of questions to intents is time-consuming. A solution that we have developed involves semi-autonomous question-intent pairing tools that considerably decrease the human effort needed to label data. Such an approach makes working with the customer data more efficient and reduces the labeling bottleneck.

With all the advances in machine and deep learning, most algorithms rely on largely pattern-based approaches to extract intent from a large corpus of previously seen chat history. Users questions to banks differ from questions asked to telecom companies and there is no off-the-shelf algorithm to fit both cases. An optimal solution is to use a host of different algorithms (SVMs (support vector machines), Naive Bayes, LSTMs (long short-term memory), and feedforward neural networks) to match user questions to specific intents. An ensemble of predictors yields a confidence score for each intent, and you can then take the best match. Such an approach provides users with more accurate answers.

Extraction of meaning or more specifically, semantic relations between words in free text is a complex task. The complexity is mostly due to the rich web of relations between the conceptual entities the words represent.

For example, a simple sentence like my older brother rides the bike contains a lot of semantic richness, as the hidden baggage is not evident from the tokenized surface representation (e.g. my brother is a human, the bike is not a living entity, my brother and I likely have the same mother/father, I am younger than my brother, and the bike cannot ride my brother).

Shared collectively, this knowledge makes communication with others possible. Without it, there is no consistent interpretation and no mutual understanding. When reading a piece of text, youre not just looking at the symbols but actually mapping them to your own conceptual representation of the world. It is this mapping that makes the text meaningful. A sentence will be considered nonsensical if mismatches are found during the mapping.

Since the computers manufactured today do not include a model of the world as part of their operating system, they are also largely clueless when fed unstructured data, such as free text. The way a computer sees it, a sentence is just a sequence of symbols with no apparent relations other than ordering in the sentence. As the problems related to financial services can be rather specific, you have to augment the typical pipeline of NLP and machine learning with semantic enrichment of inputs. You must devise semantic ontologies that are helpful for the identification of users problems in the financial and telecom sectors. The underlying idea of semantic ontologies is to encode commonalities between concepts (e.g. cats and dogs are both pets) as additional information yielding a denser representation of tokens. Another step forward is architecture capable of semantic tagging of both known and unknown tokens, based on the context.

VCAs must understand the bulk of cases in which users ask a question in natural language. The VCA should be able to understand the problem and help the user resolve the problem without involving human support. For narrow and only rules-based VCAs, the resolve rates can be higher, but in our experience people are impatient when dealing with customer service. Instead of reading instant articles and suggested topics, they wish to express their problem as a specific question, and they expect a relevant answer. Understanding free text is a tough problem, and current autonomous resolve rates that hover around 10-20 percent reflect that. Even so, when considering that larger companies need hundreds of people to solve highly repetitive issues for their customers, automating even that percentage can save a lot of working hours and allow humans to focus on the more creative and demanding aspects of their work.

Indrek Vainu is the CEO and co-founder of AlphaBlues, a company automating enterprise customer service chat with artificial intelligence.

Above: The Machine Intelligence Landscape This article is part of our Artificial Intelligence series. You can download a high-resolution version of the landscape featuring 288 companies by clicking the image.

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The reality of automating customer service chat with AI today - VentureBeat