To take optimum advantage of the disruptive technologies that AI brings in, a few businesses may need to make dramatic adjustments in their company culture and the way they work. Right now, for some businesses, AI and big data are merely perceived as something that can cut operational expenses, instead of it being a crucial methodology for creating increased profit, output, and improved assurance of business success. The C-suite cannot overlook AI and big data convergence as it is needed to consolidate the business ability and critical insights.

The rapid progress of data platforms has seen advanced analytical models being used to display complex business scenarios for operations, planning, investment, and innovation. While technological capabilities are readily available everywhere,  what makes the real impact is how they are deployed to make progress.

The increasing demand for cloud and data analytics capacities encourages experimentation. It helps in ad hoc utilization to deliver quick outcomes knowing the abilities and associated dangers of having individuals with experience and knowledge. For pivotal business assignments, AI may not be granted decision-making capabilities. But, its capacity to give reliable, accurate data is as of now prompting compelling insights that change business operations entirely.

AI’s automation abilities imply it to be progressively utilized to streamline unremarkable tasks, freeing up resources to focus on high-level activities. This can add to process efficiencies by improving profitability and bringing down operating expenses.

As AI gets enriched from new data inputs, it will turn out to be progressively ground-breaking and ready to simplify complex tasks and algorithms. It will further create growth opportunities for collaboration and increased efficiency. ML is helping AI applications to comprehend a more extensive scope of guidelines better, clarify the context by understanding the need better.

While the pace of technological change is uncertain, one thing is sure. It will continue to gather pace, driving innovative systems, new processes and efficiencies, delivering new solutions and products. The ability to recognize and fuse the best solution for business growth, and at the right time to increase advantage, is a significant challenge.

Firms must guarantee a well-structured architecture framework that empowers CIOs to respond with the required flexibility to join the new and replace the old. As AI applications are becoming progressively intricate and more ingrained in daily life, there will be an increased requirement for people to clarify the discoveries and decisions by a machine.

Supervision of AI applications will be crucial to ensure that undesirable results, for instance, discrimination, are recognized and prevented. Regardless of how perfect AI becomes, it will always require human intelligence and guidance to discover innovative solutions to satisfy its intended function.

Even though AI offers immense opportunities for improvement and innovation, it can’t achieve its full potential on its own. A community future will see engineers, programmers, data scientists, workers, and everyday consumers completely integrating AI into their daily lives.

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After running the industrial robot operating system startup “Cobot” for three years, founder Miao Li has decided to axe 98 percent of the company’s “fancy services” and just focus on drug logistics and sorting mushrooms.

Although the countless pages of operating system manuals he’d produced represented an investment of over US$15 million, Miao believes his company will be better served by perfecting “one platform (and OS) and two scenarios (smart detection and flexible grasping)”.

Miao’s entrepreneurial journey started off smoothly and successfully. He founded Cobot in May 2016 and by late 2017 the company had amassed over US$15 million in series B funding led by GGV Capital and Lan Fund. This spring Cobot visited the Hannover Messe industrial fair in Germany to showcase its flagship AI-implanted manipulation system “COBOTSYS.”

Even a young robotics software startup however will have research initiatives that just get “swept under the carpet”. In just the past year, Cobot explored then abandoned robotics solutions for electronics, auto parts, food, logistics, healthcare and R&D. Miao says 99 percent of the time gets wasted on “dirty work” and hardly-useful cases.

Developing a simple classification system for example for sorting shiitake mushrooms can be fraught with frustrations. There is no existing data for this task, typically it’s done manually by human labour. Miao says the first 100 gigabytes of mushroom image data collected by Cobat researchers had to be trashed due to insufficient lighting. The team then had to have human experts correct errors with their data labelling. Only after solving these and other issues was Cobat able to get their AI-powered system’s recognition accuracy up to 90-95 percent.

As an operating system startup, Cobot is in a less dominant position than hardware vendors or system integration companies when it comes down to negotiating deals. Third-party companies also tend to get paid last.

The Chinese industrial robotics market is tough for startups to crack. According to data from Xinhua News, 2018 saw cumulative sales of 156,000 robot units, 70 percent of which were imported. Moreover, scaled production usually refers to shipments of 10,000 units and above, a volume beyond the ability of most startups. Many are suffering from negative profits or relying heavily on government subsidies.

Before startups like Cobot emerged, the industrial robot market was firmly in the grip of four industry giants: Fanuc, ABB, Kuka and Yaskawa. There are also established competitors such as Japan’s Nachi-Fujikoshi, Kawasaki Heavy Industries, Europe’s DURR and SASMES, and Adept from the US.

In China the big four industrial robot companies have about 49.7 percent of the market, with particularly strong advantages in auto and electronics manufacturing. Startups such as Cobot can only survive by offering highly differentiated products and services, and Miao decided to carve out a niche in traditional industrial scenarios that were data-poor.

Miao’s team is now adding drug logistics to its automated industrial applications. At a hospital in Shekou, Shenzhen, Cobot’s flexible grappling robot is sorting medicines at a rate of 800 boxes/hour, with recognition accuracy of 99.8 percent. The system also uses data such as batch numbers to ensure drug shipments based are delivered within expiration dates.

“The industry is trying hard to make sales pitches for robotics operating systems,” says Miao, “and this means convincing them with real statistics. For instance, our recognition accuracy for shiitake mushrooms is now 99 percent. A factory that installs five mushroom lines can replace 80 workers, saving up to US$400,000 and breaking even on automation cost within a year.”

The road ahead will not be easy for startups like Cobot. Global industrial robot revenue growth was just 0.9 percent in 2019, and net profits fell by 39 percent. Already impacted by slow growth economies and sluggish automobile and electronics sales, the robotics industry is anticipating further setbacks. To make matters worse, even as robot industry revenue streams dry up, the sector’s average R&D costs rose this year by 12.4 percent.

Cobot systems are currently in use in more than a dozen hospitals in China, mainly major, financially-healthy hospitals in Shenzhen, Guangzhou, and Zhongshan. Although the past few years have seen anxiety and hardship ripple through the industry, Miao remains confident he’s on the right track: “There is a definite future for robotics operating systems.”

The post Running an Industrial Robotics Software Startup Is Not Getting Easier appeared first on Artificial Intelligence.

From Amazon’s Alexa learning which restaurants its users like, to Apple’s iPhone predicting the next word in a text message, artificial intelligence (AI) is already having a significant influence on everyday life.

But Northwestern economist Benjamin Jones and his colleagues are now asking what happens to economic growth if artificial intelligence starts generating original thought. They are among the researchers looking at how much more human work AI can automate, including the generation of new ideas.

“If machine learning can really take over all human tasks and take over ideas of innovation, then it would be possible to get a radical change in the growth rate” of the economy, Jones told CNBC in an interview. “But the real question is going to be: can AI take over all of the essential tasks?”

Jones, along with Chad Jones of Stanford University and Philippe Aghion of the College de France wrote about their research in a paper entitled “Artificial Intelligence and Economic Growth” for the National Bureau of Economic Research earlier this month.

If rapidly-improving artificial intelligence can provide the markets with innovations to improve the workplace, some jobs could see skyrocketing wage growth while others could become obsolete.

AI activity has been accelerating, with the world’s top technology companies leading the way. Self-driving vehicles have been one popular subject of experimentation. Chipmakers including Nvidia have refined their products to better suit AI computations, while Amazon has long used AI to recommend products in its e-commerce business.

This week, Google-owned DeepMind published the latest findings of Alphago, its project in which a computer learns how to play the board game Go. This latest installment, dubbed Alpha Zero, managed to beat Google’s existing AlphaGo 100 times consecutively after only three days of training. This happened completely without human training.

Jones and his research team looked at different scenarios. The first modeled growth if people could be replaced by AI in all tasks. Other models looked at growth with partial automation. There weren’t any stark numerical findings, but the ongoing research is aimed at finding how AI can be useful in generating economic growth, as the steam engine did in the 1800s and early computer chips did in the middle 20th century.

In one model, replacing labor with artificial intelligence, the research team showed that only AI and economic capital could be required for the generation of new ideas.

Some have hypothesized that AI could enter into a rapid cycle of self-improvement, with each new cycle more intelligent than the previous one. Such a development could dramatically change the way people live.

But, Jones said, there is ongoing disagreement among economists on whether AI can — or even should — reach the point where it can generate original ideas. One of the most important lessons of their research is that economic growth may be constrained not by what humans are good at, but rather by tasks that are essential yet hard to improve.

In farming, for example, while fertilizer and combines boosted growth for a while, a finite amount of arable land has kept production bounded.

“The way to think about it is bottlenecks,” explained Jones. “We are vastly better at growing food than we were 100 years ago, but by virtue of automation it now only accounts for 2 percent of GDP.”

“We have computers that are mind-bogglingly fast,” continued Jones. “And yet, growth recently has been slower than it has been. Our limit to economic performance probably isn’t computation, right? We’re still heavily constrained by things we can’t or find harder to improve.”

Another possible bottleneck is what the researchers called “search limits.”

The idea suggests that the most obvious innovation ideas are discovered first and new ideas become increasingly harder to find. While AI could help speed up that search, it may be subject to a finite universe of new ideas.

Still, Jones said he is excited about the research.

“What’s interesting about AI is that it seems like we’re on the edge of tasks that are cognitive,” he said.

To be sure, a number of innovators and scientists don’t believe artificial intelligence is a great idea.

Tesla and SpaceX CEO Elon Musk has repeatedly warned against AI, going so far as to declare that competition for the technology will be the “most likely cause of World War III.” Earlier this year, Musk said humans must somehow merge with machines or risk becoming irrelevant in the age of AI. The billionaire is working on a company called Neuralink to do just that.

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