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9 Developments In AI That You Really Need to Know

Over 6,000 people are attending a conference focusing on artificial intelligence (AI) that opened in Amsterdam this morning.

Participants will be learning about some of the latest innovations in AI – the creation of human-like technology -that will transform business and the ethical issues that come with it.

The event coincides with Artificial Intelligence in Europe, a report by Microsoft that reveals over half of the companies surveyed expect AI to have an impact on “business areas that are entirely unknown today”.

1.Just get on with it “Understanding an enterprise’s AI maturity is the difference between a company that aims too high with AI and fails, versus a company that finds the best entry point to start with AI and expand from there,” says Joseph Sirosh, Corporate VP and CTO AI, Microsoft.

'CEOs must also identify the right business opportunity for AI – enhancing customer service, improving productivity, reducing manufacturing defects or something completely unique.'

The majority of business are following Sirosh’s advice, according to the Microsoft report, with 61% of companies surveyed either planning or piloting AI already.

“But we can actually use AI to forceourselves to do what we previously chose to ignore.” When we do, a new level of transparency will lay bare our prejudices and preferences.

“We need to ensure we develop algorithms that do not stop people from getting mortgages because of the colour of their skin.” 3.Human resources Humans are good at strategy and big pictures.

“How can a farmer, who is dealing with a meaningful problem, connect with a city dweller who might have the solution?” says Amir Banifatemi, generalmanagerforinnovation at Xprize and managing partner K5 Ventures.

“In the U.S., it willbe more integrated with the consumer.” As the technology leaps forward, the problem is whether the consumer knows whether they are talking to an AI-powered chatbox or a human.

If we do not cultivate public trust, it will be an AI winter.” The Foundation for Responsible Robotics, in partnership with consultants Deloitte, launched a quality mark for robotics and AI only last month, giving a product a rating out of three with a logo confirming certification.

“Just think of drone technology.” Order a service or a product, however innocently, without knowing its dual use and the potential damage to a corporate’s reputation will be immense.

He’s just returned from Turing Town, Hanzhong, China, which boasts space for 500 AI companies and where the success of your career, as a local politician, is linked to your ability to attract AI businesses.

Silicon Valley Company unveils revolutionary Artificial Intelligence (AI) driven Processor Generator

The generator is available for viewing and experimenting at the ARM Techcon on October 17-18, 2018 in San Jose, CA “Selecting the right processor, configuring multi-cores and establishing the right topology is very challenging for the new breed of systems,”

The generated model supports variable processor pipelines, SIMD/MIMD, multi-thread, multi-level cache hierarchy, coherency, heterogeneous execution units, buffers and bus interfaces.

Mirabilis Design has generated models for over 45 processors including VisualSim Artificial Intelligence-driven Processor Generator has been available for the last six months to a limited number of current Mirabilis Design users.

About Mirabilis Design Mirabilis Design, a Silicon Valley company, designs cutting edge software solutions that identify and eliminate risks in product performance.

Its flagship product, VisualSim Architect is a system-level modeling, simulation, and analysis environment that relies on libraries and application templates to vastly improve model construction and time required for analysis.

The seamless design framework facilitates designers to work on a design together, cohesively, to meet an intermeshed time and power requirements.

Can Artificial Intelligence Help Feed The World?

Borlaug’s introduction of disease resistant high-yielding crop varieties and advanced agricultural practices was a game changer, as agriculture yields increased tremendously and helped save millions from starvation.

At the same time, world population continues to grow and is projected to reach at least 9 billion people by 2050, much of the growth is clustered in developing countries, where rapid economic expansion is allowing for increased calorie availability and consumption with an increased demand for protein.

As these two forces of population growth and demand for food gain momentum, is a risk of reaching a Malthusian doomsday—a scenario where population growth outpaces the growth in food supply resulting in large-scale famines—becoming increasingly likely?

To balance the sometimes-opposing goals of increasing production and conserving resources, researchers and entrepreneurs are working on ways to sustainably intensify agriculture on its existing footprint.

Applications of AI today are primarily being driven by the tech sector, with use cases ranging from enhanced information security to mobile ad placement, to autonomous vehicles.

Five years ago, Google sponsored researchers made a breakthrough in AI when their neural network software taught itself to detect shapes of cats and humans with >70% accuracy.

This activity has mostly been led by the big tech giants like Google, Facebook, Microsoft, and Amazon as they look to gain access to capabilities to help transform industries as diverse as transportation, healthcare, retail, and manufacturing.

Headwinds for AI in AgTech While AI has become a mainstay of the tech community, many of the major ag input companies, equipment manufacturers, and service providers have yet to vigorously pursue AI applications in agriculture.

lack of standards, perceived poor transparency around data use and ownership, and the difficulty of gathering and sharing data has lead to a situation where AI algorithm developers in Ag are still starved for data.

Farms have historically lacked the information technology infrastructure and data warehousing systems that Silicon Valley tech firms have relied on to develop and implement AI applications.

Instead, these companies have used “lean” methods to get to market quickly with a small subset of customers, following the playbook for building a tech startup.

While the lean method has worked well in software, in agriculture, a grower simply can’t risk adopting a new technology across their whole farm that may not work.

Before launching a product, major agricultural companies put their products through years of field trials to ensure consistent performance and clear benefit.

Thus, the pervasive “get to market fast” and “scale quickly” mentality may need to be tempered by a more progressive and incremental product launch strategy.

A common complaint amongst the ag-tech startup community is that it is often extremely hard to find AI talent, in light of competition with employers in the software, internet, and autonomous vehicle sectors.

Abundant, a Google Ventures funded company, uses machine vision to detect the location of apples grown on a trellis then targets a vacuum system to pull the apples off the branch.

Resson, a Monsanto Growth Ventures (MGV) portfolio company with offices in Canada and San Jose, has developed image recognition algorithms that can detect and classify plant pests and disease more accurately than a trained human.

They have shown that they can reduce herbicide usage by 90% by moving from a broadcast spraying methodology, to a highly precise and targeted spray application.

Per a 2016 Philips McDougall analysis, bringing a single new crop protection product to market now requires over 11 years of discovery and development, with the analysis of 160,000 compounds and expenditures of over $280 M per commercial product.

For example, Monsanto and Atomwise, a startup utilizing AI to develop novel therapeutics for hard to treat diseases, formed a unique research collaboration to increase the speed and probability of discovering new crop protection products.

This collaboration is leveraging AI based pattern recognition to reduce the amount of trial-and-error based laboratory testing in early stage chemical discovery.

To accelerate discovery of novel proteins for next-generation insect-control solutions, Monsanto is providing access to its extensive genomic databases and utilizing Second Genome’s expertise in analyzing microbial function through big data metagenomics, machine learning, and predictive analytics.

To help alleviate these constraints, AI researchers at Monsanto have developed an algorithm that can evaluate breeding decisions and predict which hybrids will show the best performance in the first year of field testing.

Mike Graham, Global Breeding Lead at Monsanto, has indicated that this algorithm has taken a year out of the breeding process, enabling breeders to get their best ideas into large scale field trial more quickly.

This algorithm not only accelerates the breeding process, but also has enabled Monsanto to scale the size of its corn breeding pipeline by five-fold versus the conventional methods.

CropOS utilizes data from a variety of sources such as DNA and RNA sequence information, field trial observations, and imaging analytics to predict patterns of gene expression needed to get a specific phenotypic response.

On the academic front, researchers at Carnegie Mellon University are developing a new initiative called FarmView which uses AI tools to combine plant phenotype data with genetic and environmental data to help breeders and geneticists better understand the relationships between genetics, environment, and crop performance.

IPO Season: China’s First Wave of AI Companies Are Going Public

Liulishuo raised US$72 million under an estimated market cap of US$600 million — the latest among a growing number of privately-owned Chinese AI companies that are rewarding their investors with an IPO.

Billed as China’s first AI English teacher, Liulishuo’s flagship mobile app “English Liulishuo” (Mandarin for “speak English fluently”) uses AI techniques such as deep learning and speech recognition to assess learner proficiency, provide real-time feedback on performance, and curate and deliver educational content in a personalized manner.

Also announcing an IPO on the New York Stock Exchange last week was Shanghai-based CooTek (NYSE: CTK), a mobile software company known for its AI-powered mobile keyboard TouchPal, which boasts highly accurate autocorrect and word prediction capabilities.

According to a Dealogic report, 26 Chinese tech companies offered US$8.5 billion worth of new shares in the first half of 2018, accounting for nine percent of global IPO volume.

More IPOs are expected from leading Chinese AI companies later this year and early next, including Alibaba Group fintech affiliate Ant Financial, the world’s most valuable AI company SenseTime, and China’s biggest robotic unicorn UBTECH.

Many Chinese venture capitalists and private equities have encountered obstacles in raising funds, due in part to new government capital regulations imposing additional restrictions on private funding.

That means the total amount of investors who are able to pour money out of their pocket will shrink.” The Matthew effect also applies to AI companies, suggests Yuan Gong, a China Growth Capital venture investor eyeing AI and big data.

After raising their Series B and C rounds, the top three companies in each industry can be expected to receive 70 to 80 percent of the money, or even more.” For global investors unaware or doubtful of China’s growing AI might, IPOs provide an ideal showcase for the top companies in the field.

Robots will destroy our jobs – and we're not ready for it

While robots have been utilized in several industries, including the automotive and manufacturing sectors, for decades, experts now predict that a tipping point in robotic deployments is imminent – and that much of the developed world simply isn’t prepared for such a radical transition.

However, in a classic example of optimism bias, while approximately two-thirds of Americans believe that robots will inevitably perform most of the work currently done by human beings during the next 50 years, about 80% also believe their current jobs will either “definitely” or “probably” exist in their current form within the same timeframe.

In 2015, San Francisco-based startup Simbe Robotics unveiled Tally, a robot the company describes as “the world’s first fully autonomous shelf auditing and analytics solution” that roams supermarket aisles alongside human shoppers during regular business hours and ensures that goods are adequately stocked, placed and priced.

Another study, conducted by the International Labor Organization, states that as many as 137m workers across Cambodia, Indonesia, the Philippines, Thailand and Vietnam – approximately 56% of the total workforce of those countries – are at risk of displacement by robots, particularly workers in the garment manufacturing industry.

That’s just not the case any more.” Meanwhile, developments in motion control, sensor technologies, and artificial intelligence will inevitably give rise to an entirely new class of robots aimed primarily at consumer markets – robots the likes of which we have never seen before.

This, according to Zhang, represents an unparalleled opportunity for companies positioned to take advantage of this shift, yet it also poses significant challenges, such as the necessity of new regulatory frameworks to ensure our safety and privacy – precisely the kind of essential regulation that Trump spoke out against so vociferously on the campaign trail.

DeVos and her husband Dick have spent millions of their vast personal fortune fighting against regulations to make charter schools more accountable, campaigned tirelessly to expand charter school voucher programs, and sought to strip teachers’ unions of their collective bargaining rights – including teachers’ right to strike.

Private schools such as Carnegie Mellon University, for example, may be able to offer state-of-the-art robotics laboratories to students, but the same cannot be said for community colleges and vocational schools that offer the kind of training programs that workers displaced by robots would be forced to rely upon.

He told me of his admiration for the work of libertarian journalist Henry Hazlitt, his ambitions to become a world-renowned roboticist and technologist (“I’m 21 now, so if by the time I’m 45 – Elon Musk’s age – I’ve established myself as a world-class mechatronics engineer, I’ll consider myself pretty successful”) and that he doesn’t believe everyone should go to college.

“There’s a lot of side projects I could work on that might provide more value to my future than some of the classes I take, so it’s hard to justify.” Daniel also told me that his experiences defy conventional wisdom that earning a college degree is the only pathway to success in today’s savagely competitive job market.

“In the age of Udacity, Udemy, MIT’s OpenCourseWare, it’s very possible to do a bunch of small personal projects, display that experience to an employer, and get hired.” This appetite for alternatives to traditional higher education has driven intense interest in private programming schools and self-styled coding “boot camps” in recent years.

Most [coding] schools are expensive, which means they tend not to be serving populations that need a leg up economically “I think programming boot camps have been fairly criticized for the fact that there’s a lot of tension around the idea of economic mobility,” says Adam Enbar, a former venture capitalist and co-founder of Flatiron School, one of the most renowned private programming schools in New York City.

We don’t want four math majors sitting around a table together working on a project – we’d rather have a math major and a poet, a military veteran and a lawyer, because it’s more interesting.” Developing a new iOS app may be more interesting than navigating the comparatively dreary worlds of logistics infrastructure, manufacturing protocols, and supply chain efficiencies, but America doesn’t need any more messaging or food delivery apps – it needs engineers.

The answer is that we’re not trying to create a nation of software engineers – it’s that this is becoming a fundamental skill that is necessary for any job you want to do in the future.” Despite these grave threats, when I asked Daniel where he sees himself in five years, he remained cautiously optimistic.

I’m not sure if I’ll be working at a company or for myself – that largely depends on the opportunities I find once I graduate, and it’s pretty difficult to predict that.” It is indeed difficult to predict how the gradual automation of the American workforce will take shape under Trump’s presidency.

One certainty, however, is that the interests of those Americans at greatest risk of professional obsolescence will continue to be sacrificed in favor of serving, protecting and benefiting wealthy, white conservatives – a trend we are likely to see across virtually every aspect of life in Trump’s America and yet another betrayal of the predominantly working-class voters who believed Trump’s empty promises on the campaign trail.

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