Farming isn’t usually considered a high-tech sector. That’s been changing in recent years thanks to new innovations in areas like artificial intelligence (AI) and robotics. Farmers who adopt high-tech solutions are often able to increase yield, cut costs, and produce healthier food.

Perhaps that is why Alphabet’s X lab, the division responsible for launching Waymo, is eyeing farming as its next target area. The company recently revealed its latest “moonshot” project. Dubbed Mineral, it focuses on a robot that inspects crops and a platform designed to analyze that data.

The combined approach introduces the concept of “big data” to the world of farming and could have profound implications.

Working the Problem

The agriculture industry has a massive challenge ahead of it. Experts predict that global hunger problems will only continue growing as the Earth’s population expands over the next decade. Mineral’s website says, “To feed the planet’s growing population, global agriculture will need to produce more food in the next 50 years than in the previous 10,000—at a time when climate change is making our crops less productive.”

Obviously, the situation is less-than-ideal. It’s clear that traditional farming approaches won’t be enough to solve this dilemma. Fortunately, technology might be able to help.

A major part of Mineral’s plan revolves around a four-wheeled robotic prototype that somewhat resembles a moon rover. The team appropriately calls it a plant buggy. It can study crops, soil, and other characteristics of the environment over a large area thanks to a suite of cameras and sensors. Those findings are then compared with satellite photos and weather data, according to Nick Statt of The Verge.

Using that wealth of information, scientists are able to predict how plants will grow by using AI models. So far, the Mineral team is testing the approach with soybean crops in Illinois and strawberry fields in California.

The company says, “Over the past few years, the plant buggy has trundled through strawberry fields in California and soybean fields in Illinois, gathering high quality images of each plant and counting and classifying every berry and every bean. To date, the team has analyzed a range of crops like melons, berries, lettuce, oilseeds, oats and barley—from sprout to harvest.”

Data-Driven Farming

It’s clear that Mineral’s approach has real-world implications that are important for humanity’s fight against hunger. The idea of using tech to optimize plant growth isn’t something that Mineral is alone in pursing.

A number of other startups are exploring the same space. Some are even turning to alternative approaches like hydroponics and vertical agriculture to further enhance the effects of data-driven farming.

In the days to come, Mineral will continue to partner with farmers around the world to improve its system and adapt it to meet the unique needs of various agriculture segments.

Project lead Elliott Grant says, “Just as the microscope led to a transformation in how diseases are detected and managed, we hope that better tools will enable the agriculture industry to transform how food is grown.”

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Molecules are the building blocks of everyday life. Many materials are composed of them, a little like a LEGO model consists of a multitude of different bricks. But while individual LEGO bricks can be simply shifted or removed, this is not so easy in the nanoworld. Atoms and molecules behave in a completely different way to macroscopic objects and each brick requires its own “instruction manual”. Scientists from Jülich and Berlin have now developed an artificial intelligence system that autonomously learns how to grip and move individual molecules using a scanning tunnelling microscope. The method, which has been published in Science Advances, is not only relevant for research but also for novel production technologies such as molecular 3D printing.

Rapid prototyping, the fast and cost-effective production of prototypes or models – better known as 3D printing – has long since established itself as an important tool for industry. “If this concept could be transferred to the nanoscale to allow individual molecules to be specifically put together or separated again just like LEGO bricks, the possibilities would be almost endless, given that there are around 1060 conceivable types of molecule,” explains Dr. Christian Wagner, head of the ERC working group on molecular manipulation at Forschungszentrum Jülich.

There is one problem, however. Although the scanning tunnelling microscope is a useful tool for shifting individual molecules back and forth, a special custom “recipe” is always required in order to guide the tip of the microscope to arrange molecules spatially in a targeted manner. This recipe can neither be calculated, nor deduced by intuition – the mechanics on the nanoscale are simply too variable and complex. After all, the tip of the microscope is ultimately not a flexible gripper, but rather a rigid cone. The molecules merely adhere lightly to the microscope tip and can only be put in the right place through sophisticated movement patterns.

“To date, such targeted movement of molecules has only been possible by hand, through trial and error. But with the help of a self-learning, autonomous software control system, we have now succeeded for the first time in finding a solution for this diversity and variability on the nanoscale, and in automating this process,” says a delighted Prof. Dr. Stefan Tautz, head of Jülich’s Quantum Nanoscience institute.

The key to this development lies in so-called reinforcement learning, a special variant of machine learning. “We do not prescribe a solution pathway for the software agent, but rather reward success and penalize failure,” explains Prof. Dr. Klaus-Robert Müller, head of the Machine Learning department at TU Berlin. The algorithm repeatedly tries to solve the task at hand and learns from its experiences. The general public first became aware of reinforcement learning a few years ago through AlphaGo Zero. This artificial intelligence system autonomously developed strategies for winning the highly complex game of Go without studying human players – and after just a few days, it was able to beat professional Go players.

“In our case, the agent was given the task of removing individual molecules from a layer in which they are held by a complex network of chemical bonds. To be precise, these were perylene molecules, such as those used in dyes and organic light-emitting diodes,” explains Dr. Christian Wagner. The special challenge here is that the force required to move them must never exceed the strength of the bond with which the tip of the scanning tunnelling microscope attracts the molecule, since this bond would otherwise break. “The microscope tip therefore has to execute a special movement pattern, which we previously had to discover by hand, quite literally,” Wagner adds. While the software agent initially performs completely random movement actions that break the bond between the tip of the microscope and the molecule, over time it develops rules as to which movement is the most promising for success in which situation and therefore gets better with each cycle.

However, the use of reinforcement learning in the nanoscopic range brings with it additional challenges. The metal atoms that make up the tip of the scanning tunnelling microscope can end up shifting slightly, which alters the bond strength to the molecule each time. “Every new attempt makes the risk of a change and thus the breakage of the bond between tip and molecule greater. The software agent is therefore forced to learn particularly quickly, since its experiences can become obsolete at any time,” Prof. Dr. Stefan Tautz explains. “It’s a little as if the road network, traffic laws, bodywork, and rules for operating the vehicle are constantly changing while driving autonomously.” The researchers have overcome this challenge by making the software learn a simple model of the environment in which the manipulation takes place in parallel with the initial cycles. The agent then simultaneously trains both in reality and in its own model, which has the effect of significantly accelerating the learning process.

“This is the first time ever that we have succeeded in bringing together artificial intelligence and nanotechnology,” emphasizes Klaus-Robert Müller. “Up until now, this has only been a ‘proof of principle’,” Tautz adds. “However, we are confident that our work will pave the way for the robot-assisted automated construction of functional supramolecular structures, such as molecular transistors, memory cells, or qubits – with a speed, precision, and reliability far in excess of what is currently possible.”

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TOKYO (Reuters) – In August, a robot vaguely resembling a kangaroo will begin stacking sandwiches, drinks and ready meals on shelves at a Japanese convenience store in a test its maker, Telexistence, hopes will help trigger a wave of retail automation.

Following that trial, store operator FamilyMart says it plans to use robot workers at 20 stores around Tokyo by 2022. At first, people will operate them remotely – until the machines’ artificial intelligence (AI) can learn to mimic human movements. Rival convenience store chain Lawson is deploying its first robot in September, according to Telexistence.

“It advances the scope and scale of human existence,” the robot maker’s chief executive, Jin Tomioka, said as he explained how its technology lets people sense and experience places other than where they are.

The idea, dubbed telexistence, was first proposed by the start up’s co-founder, University of Tokyo professor Susumu Tachi, four decades ago.

Their company has received funding from technology investment company Softbank Group and cell phone service operator KDDI in Japan, with overseas investors including European passenger aircraft maker Airbus SE. It dubbed its robot the Model T, a nod to the Ford Motor car that began the era of mass motoring a century ago.

Its quirky design is meant to help shoppers feel at ease because people can feel uncomfortable around robots that look too human.

ESCAPING FACTORIES

Robots are still a rare sight in public. Although they can outperform humans in manufacturing plants built around them, they struggle with simple tasks in more unpredictable urban settings.

Solving that performance problem could help businesses in industrialised nations, particularly those in rapidly ageing Japan, cope with fewer workers. Firms hit by the coronavirus outbreak may also need to operate with fewer people.

Since the outbreak started, hotels, restaurants and even gas and oil companies have contacted Telexistence, Tomioka said.

“It’s difficult to tell now what impact robots might have in restaurants – it could mean fewer people, but it could also create new jobs,” said Niki Harada, an official at Japan’s Restaurant Workers Union.

Using human operators with virtual reality goggles and motion-sensor controls to train its machines slashes the cost of retail robotics compared with complex programming that can cost 10 times more than as the hardware and take months to complete, Telexistence says.

Although FamilyMart will still need humans to control its robots, operators can be anywhere and include people who would not normally work in stores, said Tomohiro Kano, a general manager in charge of franchise development.

“There are about 1.6 million people in Japan, who for various reasons are not active in the workforce,” he said.

Future telexistence robots could also be used in hospitals so doctors could perform operations from remote locations, predicted Professor Takeo Kanade, an AI and robotics scientist at Carnegie Mellon University in the United States, who joined Telexistence in February as an adviser.

It might take another 20 years before robots can work in people’s homes, however, he said.

“In order for robots to be really usable at home we really have to be able to communicate. The fundamental thing that is lacking is knowing how humans behave.”

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Business delays caused by the Covid-19 crisis will eliminate an estimated $450 million of mobile robot revenues in 2020, but the sector is forecast to bounce back in 2021 and recoup more than the difference over the long term, a new study says.

The loss of sales in autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) is being caused by delays in both new project signoffs and in project commissioning due to social distancing measures, according to Ash Sharma, research director at the research firm Interact Analysis.

However, that impact of the pandemic will eventually be overturned by continuing trends like the increased adoption of e-commerce, moves to local manufacturing, and desire to reduce dependency on human labor, concludes the report, “Covid-19 Will Eliminate $0.5 billion Mobile Robot Revenues in 2020, but Market Predicted to Nearly Double in 2021.”

“Despite the short-term challenges caused by the pandemic, we predict that the ‘lost’ revenue in 2020 will be more than overturned over the next three years and 2023 revenues will be nearly $600 million higher than they would have been if the pandemic had not occurred,” Sharma wrote in the report.

In the meantime, the robot producers most heavily impacted by the pandemic will be vendors focused on durable manufacturing (particularly automotive), which typically require higher capital expenditure demand more complex integration and commissioning. Adding to those troubles, the biggest current adopters of robotics–apparel and general merchandise–also comprise the vendors that are suffering the most from the pandemic, he said.

By the same logic, the pandemic has actually created new opportunities for those mobile robot vendors that offer low-cost approaches, such as rentals and robotics as a service (RaaS), which now present extremely compelling solutions that allow a fast ramp-up for retailers, Sharma said. Similarly, some AMR vendors are able to ship their robots and provide commissioning support remotely to circumvent any social distancing restrictions.

Therefore, demand will continue to be reasonably high in 2020 for order fulfillment AMRs, such as goods-to-person, person-to-goods, and sortation robots. While installations and revenues will likely be significantly lower than they could have been prior to the virus, the Interact Analysis forecast still predicts nearly 70% growth in revenues for those players. Looking farther into the future, the report says that AGV revenues are predicted to grow by more than 70% in 2021 after a flat year in 2020, and AMR revenue growth will be even more impressive, predicted to double in 2021.

The report follows an ongoing debate over whether that rising adoption of robots creates or destroys workers’ jobs.

Earlier this month, Massachusetts Institute of technology (MIT) economist Daron Acemoglu released a study finding that industrial robots replace human workers. According to the study, from 1990 to 2007, adding one additional robot per 1,000 workers reduced the national employment-to-population ratio by about 0.2%, although some areas of the U.S. were affected far more than others. This means each additional robot added in manufacturing replaced an average of 3.3 workers nationally. The increased use of robots in the workplace also lowered wages by roughly 0.4% during the same time period.

“We find fairly major negative employment effects,” Acemoglu said in an advance edition of the report, “Robots and Jobs: Evidence from U.S. Labor Markets,” which is scheduled for publication in the Journal of Political Economy. “We find negative wage effects, that workers are losing in terms of real wages in more affected areas, because robots are pretty good at competing against them.”

In response, the Germany-based trade group International Federation of Robotics (IFR) on Monday called Acemoglu’s research “wrong” and said that recent experience in the US, Europe, and Asia proves that robot adoption will more likely be a critical determinant of productivity growth for the post-Covid-19 economy.

According to IFR’s statistics, employment in the U.S. automotive industry – the nation’s largest adopter of robots – increased by 22% from 824,400 to 1,005,000 jobs between 2013 and 2018, despite a rise in the global operational stock of robots by about 65% to 2.4 million units over the same period.

“The impact of automation on employment is not in any respect different from previous waves of technology-driven change,” IFR President Milton Guerry said in a release. “Productivity increases and competitive advantages of automation don’t replace jobs – they will automate tasks, augment jobs and create new ones.”

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Spot, the robotic “dog” design from Boston Dynamics, has had a busy pandemic, between counseling patients and enforcing social distancing guidelines. Now, a new partnership with a New Zealand robotics firm is setting up the four-legged automaton for a new line of work: farming.

Technically, the partnership is much bigger than that. Rocos specializes in the remote monitoring and operation of robot fleets. By working together, the capabilities of Boston Dynamics robots like Spot will expand thanks to human operators who can manage their performance from a great distance.

Think of the farming thing as a proof-of-concept. A video released by Rocos shows Spot exploring rough terrain, inspecting crops, and herding sheep – all of which is meant to serve as a demonstration of the various ways remote operation can be used to put robots to work.

“Equipped with payloads like heat, LIDAR, gas and high resolution camera sensors, Spot navigates rugged environments to capture data in real time – feeding this data back into existing business systems, wherever they are located ,” the Rocos news announcement reads, before offering some examples.

“In the energy sector this provides real-time anomaly detection as well as access to historic digital records for comparison. In agriculture, farmers can access information such as more accurate and up-to-date yield estimates. This provides access to a new category of automation, and a safer, more efficient business.”

Automation has been creeping into industrial spaces for many years now and the coronavirus pandemic has only accelerated that shift. As the illness has come for warehouse and assembly line workers, who all tend to operate in close quarters, the question of how and where robots can step in to help has become louder and more pressing.

We were already starting to see robots pop up in supermarkets before the pandemic threat really started having an impact in the United States. Amazon is also reportedly looking at installing robots in its warehouses.

Not everyone is on board with throwing open all the doors to using robots as replacements for human labor, of course. Amazon in particular has faced harsh criticism in recent months for what many have described as unsafe warehouse working conditions. The move toward automation in general seems inevitable, but Amazon’s will face particular scrutiny over any such decision due to the stories that have come out.

Individual company stories aside, the march toward automation continues on. Boston Dyanmics’ partnership with Rocos ought to speed that along, given the capabilities of robots like Spot and all the potential ways remote operation could put them to work.

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By getting inspired by the biomechanics of cheetahs, scientists at North Carolina State University have developed the fastest ever soft robot that can move quickly on concrete surfaces or in the water than previous generations of soft robots.

What’s more, it can delicately grab objects or with sufficient strength to lift heavy objects.

The robot is 7 centimeters long and weighs about 45 grams. It has a spring-powered, ‘bistable’ spine. This means it has two stable states.

Jie Yin, an assistant professor of mechanical and aerospace engineering at North Carolina State University, said, “We can switch between these stable states rapidly by pumping air into channels that line the soft, silicone robot. Switching between the two states releases a significant amount of energy, allowing the robot to exert force against the ground quickly. This enables the robot to gallop across the surface, meaning that its feet leave the ground.”

“Previous soft robots were crawlers, remaining in contact with the ground at all times. This limits their speed.”

The new class of soft robots, which are called “Leveraging Elastic instabilities for Amplified Performance” (LEAP), can reach speeds of up to 2.7 body lengths per second—more than three times faster—at a low actuation frequency of about 3Hz. These new robots are also capable of running up steep inclines, which can be challenging or impossible for soft robots that exert less force against the ground.”

Yin said, “We also demonstrated the use of several soft robots working together, like pincers, to grab objects. By tuning the force exerted by the robots, we were able to lift objects as delicate as an egg, as well as objects weighing 10 kilograms or more.”

Scientists noted that this work caters as a proof of idea and is idealistic that they can modify the design to make LEAP robots that are even faster quicker and more remarkable.

Yin said, “Potential applications include search and rescue technologies, where speed is essential, and industrial manufacturing robotics. For example, imagine production line robotics that is faster, but still capable of handling fragile objects.”

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Despite automation equipment, robotic arms and AGVs (automated guided vehicles), manual operation is still necessary for some manufacturing industries and thus harmonized collaboration between workers and robots is essential to Industry 4.0, according to Shinyi Fukui, executive officer for Technology Development under Omron.

Fukui made the remarks at the Robot Development & Application Expo 2020 (RoboDEX) taking place in Tokyo during February 12-14.

Japanese and other advanced countries’ manufacturers, to cope with declining labor supply and rising wages, have moved production lines to China and Southeast Asian where workforce is abundant, Fukui said. However, moving of production lines is not the optimal solution, and instead hiking automation via adoption of robots and AI technology is a sustainable solution, Fukui noted.

While Industry 4.0 is mostly thought of as development of automation toward unmanned factory, not all manufacturing industries can realize that because robots and AI technology are unable to replace many types of manual work, Fukui explained. Omron aims to transform human-robot interaction from mutual replacement to mutual cooperation, Fukui indicated, adding this is the goal of Industry 4.0 for many manufacturing industries.

For realizing human-robot collaboration, 5G communication technology plays a key role, for its high-speed data transfer in large volumes enables analysis of detailed movement of human bodies. When robots can understand the characteristics of human behavior via analysis of real-time images and algorithm-based computing, they can ideally collaborate with workers, Fukui said. By virtue of low latency and multi-point connectivity, 5G technology enables wide deployment of IoT sensors and hikes efficiency in data collection, Fukui said.

Omron has begun human-robot collaboration at its factories, Fukui noted. For example, AGVs carrying components and materials can run to each worker and avoid collision through machine learning-based sensing of surrounding physical changes, Fukui said. In addition, IoT sensors collect data on workers’ operating conditions to let them understand their working performance and what can be improved, Fukui noted. This can not only hike working efficiency in man power but also save time and cost for training workers, Fukui indicated.

Omron will keep cooperating with NTT DoCoMo, Nokia and other telecom carriers to seek more applications of 5G technology to human-robot collaboration.

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Retailers are increasingly adopting artificial intelligence and robotics, both in brick-and-mortar shops and in warehouses, and with a new robot workforce comes the need for new management methods.

That’s right: in the near future, HR departments won’t focus only on human employees, but also include a robot resources department to look after non-human workers.

According to research firm Gartner, robot resources could be a thing as early as 2025. In the next five years, predict Gartner analysts, at least two of the top ten global retailers will have reshuffled their HR departments to accommodate the needs of their new robot workers.

There is no need to start thinking about AI holidays and robot retirement parties. Rather, robot resources organizations will be procuring, maintaining, training, taxing, decommissioning and disposing of obsolete machinery.

With AI-powered robots being particularly suited to the retail industry, Gartner’s research predicts that 77% of retailers plan to deploy AI as early as 2021. Automation of tasks such as floor cleaning, data-collection or security could have promising results – and the very first use case identified by the research firm is warehouse picking.

Big retailers have already demonstrated the potential of scaling AI and robotics in the warehouse. Walmart, for example, recently unveiled Alphabot, a robotic fulfillment system implemented in the retailer’s 20,000 square-foot warehouse in New Hampshire, and which combines human labor and robot speed to pick 800 products per hour.

Similarly, US giant Kroger signed a deal in 2018 with UK company Ocado to build huge automated robot warehouses, in which dishwasher-sized robots coordinate in swarm-like behavior to pick orders before handing them over to human employees to pack into bags.

It is largely customer demand for both accuracy and speed that has boosted retailers’ interest in robotics. “The retail industry continues to transform through a period of unprecedented change, with customer experience as the new currency,” said Gartner’s research director Kelsie Marian.

“The adoption of new digital technologies and the ever-changing expectations of customers continues to challenge traditional retailers, forcing them to investigate new human-hybrid operational models.”

But just because robot resources are coming of age, that doesn’t mean that human resources are going anywhere. Quite the opposite, argued Marian: retail workers will have to work alongside new robotic colleagues, and the new paradigm will require careful planning.

She highlighted that choosing the right candidate – human or machine – for a given job will be critical for success. “Retail CIOs must provide ongoing maintenance and performance monitoring for effectiveness,” she said. “If not, the team may become counterproductive and lead to a bad customer experience.”

In that respect, Amazon may well be an example of success. The retail giant started working with robotics as early as 2012, when it purchased robot manufacturer Kiva Systems. Working in tandem with human employees, Kiva’s robots transport pallets of inventory from one location to another in Amazon’s warehouses. More recently, they have started scanning and boxing items to be sent to customers.

Amazon said that if installed in each of its 55 US fulfillment centers, the robots could eventually replace 1,300 employees. The news sound bad, but in parallel the company announced that it would pay workers up to $10,000 to quit their jobs and set up their own delivery business, in order to tackle retail’s infamous last-mile logistics challenge.

Since the initiative was announced last year, tens of thousands of workers have applied to Amazon’s new delivery service program.

According to Gartner’s analysts, such examples of human-robot collaboration will become mainstream in future retailers’ business models. “This means the robot will have to mesh with the human team – essentially meaning that both sides will need to learn how to collaborate to operate effectively together,” she said.

Hence the importance of rethinking HR departments. The good news is that those new robotic teams shouldn’t be requiring too much feedback forms and probation paperwork.

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Transparency Market Research delivers key insights on the global deep learning chipset market. In terms of revenue, the deep learning chipset market is estimated to expand at a CAGR of ~24% during the forecast period, owing to numerous factors, regarding which TMR offers thorough insights and forecasts in its report on the deep learning chipset market.

The deep learning technology is driving advancements in artificial intelligence (AI). In the current scenario, the deep learning chipset technology is being led by graphics processing units and central processing units. However, in the next few years, other chipset types including, application-specific integrated circuits and field programmable gate arrays, are expected to play an extended role.

Deep Learning Chipset Market: Dynamics

While in the current scenario, a large number of developments are being witnessed in the robotics industry, the difference between human skills and robot motor skills still remains large. Machines still need to go a long way to match the human proficiency even at the level of basic sensorimotor skills such as grasping things. However, by linking learning with a continuous feedback, this gap can be bridged. By doing so, it would become easier for robots to understand the complexity of the current world and handle issues intelligently and reliably.

The neural network technology has made great strides to design computer programs that can process images, text, and speech, and can draw pictures too. However, introducing various actions and control mechanism adds substantial new challenges to the network. Overcoming these challenges would help in understanding the method in which machines would communicate in the current ecosystem. By bringing the power of large-scale deep learning to the robotic control, fundamental issues in robotics and automation can be easily resolved. This, in turn, is expected to augment the deep learning chipset market during the forecast period.

Deep Learning Chipset Market: Prominent Regions

North America is the dominant region in the deep learning chipset market. Growth of the market in the region can be attributed to high investments and a large number of manufacturers in the region. Moreover, the market in Europe is expected to witness significant growth during the forecast period, due to increasing demand for deep learning chipsets for use in prediction of frauds and failures in the region. Asia Pacific, South America, and Middle East & Africa are emerging markets for deep learning chipsets, offering lucrative opportunities to vendors and system integrators in the long term.

Deep Learning Chipset Market: Key Players

Key players operating in the global deep learning chipset market are IBM Corporation, Graphcore Ltd, CEVA, Inc., Advanced Micro Devices, Inc., NVIDIA Corporation, Intel Corporation, IBM Corporation, Movidius, XILINX INC., TeraDeep Inc., QUALCOMM Incorporated, and Alphabet Inc.

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Since Artificial Intelligence became a hot topic, some people started having concerns about it. Creating something which is smarter than humans and more capable of anything than humans can be pretty dangerous, according to those people.

Rich Walker, who’s the Shadow Robot Company Director, is one of the people who warn others about the unregulated development of AI technology, which can be hazardous for people.

Rich Walker talked to express.co.uk, and he said that before deciding that we should develop AI and add more of it into our lives, we must be aware of the negative sides it has.

“If that technology starts to be used we should treat it the same way we have treated other technologies people have used in desperate situations. We don’t allow people to use chemical weapons in war, we don’t allow people to use biological weapons and the use of land mines and cluster mines is heavily regulated or controlled. We have already decided that certain types of weapons are not acceptable, maybe artificial intelligence just fits into the ‘Yeah that’s not acceptable either’ bucket.”

Rich Walker isn’t alone on his opinions about the negative sides of artificial intelligence, many people say that creating artificial intelligence and putting it into effect in every aspect of our lives can be dangerous if the right precautions aren’t taken.

In January 2019, at the World AI Conference in Shanghai, Elon Musk stated his opinions on artificial intelligence clearly. He said, “I think generally, people underestimate the capability of AI. They sort of think like, it’s a smart human. But it’s, it’s really much—it’s going to be much more than that. It’ll be much smarter than the smartest human. It’ll be like, can a chimpanzee really understand humans? Not really, you know. We just seem like strange aliens. They mostly just care about other chimpanzees. And this will be how it is more or less in relativity. In fact, if the difference is only that small, that would be amazing. Probably it’s much, much greater. So like, the biggest mistake that I see artificial intelligence researchers making is assuming that they’re intelligent. Yeah, they’re not, compared to AI. And so like, a lot of them cannot imagine something smarter than themselves, but AI will be vastly smarter—vastly.”

The way artificial intelligence can be bad varies a lot. It can create autonomous weapons, can cause social manipulation via social media, invasion of privacy due to cameras that can be found everywhere and facial recognition algorithms, and so on.

RT reported that Russian President Vladimir Putin said, “Artificial intelligence is the future, not only for Russia but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world.”

According to Physicist Stephen Hawking, artificial intelligence could be the “worst event in the history of our civilization.”

Stephen Hawking is one of the people who think artificial intelligence can bring evil to our lives. He once said, “Unless we learn how to prepare for, and avoid the potential risks, AI could be the worst event in the history of our civilization. It brings dangers, like powerful autonomous weapons, or new ways for the few to oppress the many. It could bring great disruption to our economy.”

So, even though some people think that artificial intelligence can be good for our future, some scientists think it can cause the biggest harm to people if it’s not developed and programmed wisely and we ignore the potential risks. What are your opinions on artificial intelligence?

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