by Future Labs Research Group Selection | Feb 17, 2025 | Artificial intelligence
The global market value of industrial robot installations has reached an all-time high of $16.5 billion.
The International Federation of Robotics (IFR) stated that technological innovations, market forces, and new business fields will drive future demand.
The IFR also published its five global robotics trends for 2025. Here’s what they said:
Artificial Intelligence: Physical, Analytical, and Generative
The trend towards artificial intelligence (AI) in robotics is increasing. By using various AI technologies, robotics can perform a wide range of tasks more efficiently:
Analytical AI enables robots to process and analyze large amounts of data collected by their sensors. This helps manage variability and unpredictability in external environments, in high-level, low-volume productions, and in public environments. For example, robots equipped with vision systems can analyze past tasks to identify patterns and optimize their operations for greater accuracy and speed.
Robot and chip manufacturers are recently investing in developing dedicated hardware and software that simulate real-world environments. This so-called physical AI allows robots to train in virtual environments and operate based on experience rather than programming.
These generative AI projects aim to create a “ChatGPT moment” for physical AI. This AI-driven robotic simulation technology will advance in traditional industrial environments and service robotics applications.
Humanoids
Humanoid-shaped robots have received a lot of media attention. The idea is that robots will become general-purpose tools that can load a dishwasher on their own and work on an assembly line elsewhere. Startups are working on these general-purpose humanoid robots.
However, industrial manufacturers are focusing on humanoids that perform single-purpose tasks. Most of these projects are taking place in the automotive industry, which has played a key role in pioneering robot applications throughout industrial robotics history, as well as in the storage sector.
However, whether humanoid robots can represent an economically viable and scalable business model for industrial applications, especially compared to existing automation solutions, remains to be seen. Nonetheless, there are many applications that could potentially benefit from the humanoid form, offering market potential for robotics, such as logistics and warehousing.
Sustainability and Energy Efficiency
Meeting the UN’s environmental sustainability goals and corresponding regulations worldwide is becoming an important requirement for suppliers’ whitelist inclusion. Robots play a key role in helping manufacturers achieve these goals.
Overall, their ability to perform tasks with high precision reduces material waste and improves the product-to-input ratio of a manufacturing process. These automated systems ensure consistent quality, which is essential for products designed to have long lifespans and minimal maintenance.
In the production of green energy technologies, such as solar panels, electric vehicle batteries, or recycling equipment, robots are essential for achieving cost-effective production. They allow manufacturers to quickly scale production to meet growing demand without compromising quality or sustainability.
At the same time, robotics technology continues to improve to make robots more energy-efficient. For example, the lightweight construction of moving robot components reduces energy consumption. Different sleep mode levels place hardware in an energy-saving parking position. Advances in gripper technology use bionics to achieve high gripping force with almost zero energy consumption.
Robots in New Business Fields
The manufacturing industry still has significant potential for robotic automation. Most manufacturing companies are small and medium-sized enterprises (SMEs).
The adoption of industrial robots by SMEs is still hindered by high initial investment and high total cost of ownership. Robot-as-a-Service (RaaS) business models allow companies to benefit from robotic automation without fixed capital. RaaS providers specializing in specific industries or applications can quickly offer sophisticated solutions. Additionally, low-cost robotics provides options for potential customers who find high-performance robots too large for their needs. Many applications have low requirements in terms of precision, load, and lifespan. Low-cost robotics targets this new “good enough” segment.
Among the new interesting customer segments, besides manufacturing, are construction, laboratory automation, and warehousing. Demand across industries is being driven by recent crises that have raised political awareness about national production capacity in strategically important sectors. Automation enables manufacturers to relocate production without sacrificing cost efficiency.
Robots Solve Labor Shortages
According to the International Labour Organization (ILO), the global manufacturing sector continues to suffer from labor shortages. One of the main drivers is demographic change, which is already affecting labor markets in major economies such as the United States, Japan, China, South Korea, and Germany. Although the impact varies from country to country, the cumulative effect on the supply chain is a concern almost everywhere.
The use of robotics significantly reduces the impact of labor shortages in manufacturing. By automating dirty, dull, dangerous, or delicate tasks, human workers can focus on more interesting and higher-value tasks. Robots perform tedious tasks like visual quality inspection, hazardous painting, or heavy lifting. Technological innovations in robotics, such as ease of use, collaborative robots, or mobile manipulators, help fill gaps whenever and wherever necessary.
The IFR also published its five global robotics trends for 2025. Here’s what they said
by Future Labs Research Group Selection | Dec 11, 2024 | News
The United States government is demanding that Google sell its internet browser Chrome to limit the company’s monopoly in that market, a measure that would shake the tech giant.
The Department of Justice presented this antitrust recommendation to federal judge Amit Mehta in Washington, who will rule after Google’s conviction for anticompetitive practices in internet searches.
What would the impact be for Google?
“This would be a huge blow to Google,” said analyst Dan Ives from Wedbush Securities.
Google offers free searches and generates revenue through targeted advertising and online commerce features. “It would be a drastic change to the company’s business model,” said advertising professor Beth Egan from Syracuse University.
Selling Chrome would leave Google without an important source of “a lot of information they can use to train their algorithms” and promote other services like Maps, explained Egan.
Launched in 2008, Chrome captures about 70% of the online search market, surpassing its competitors Edge and Safari, developed by Microsoft and Apple, respectively.
However, experts believe Google would find a way to recover if forced to sell Chrome. “I don’t think losing the browser is going to kill Google as a company,” said Egan.
For example, Apple implemented a drastic limitation on cookies in Safari, the markers that allow companies to track user browsing.
“Advertisers said: ‘We have a deadlock, but we’ll manage,'” Egan recalled. “And Google will do the same.”
How much is Chrome worth?
A Bloomberg analyst estimates that Chrome, used by more than 3 billion people worldwide, could be sold for at least $15 billion. However, the lack of precedents makes it difficult to predict Chrome’s market value.
In 2016, a group of Chinese investors bought the Norwegian search engine Opera Software ASA for $600 million, which at the time had only 350 million users.
Who could buy it?
“Potential buyers for Chrome are really not many,” estimated senior analyst Evelyn Mitchell-Wolf from Emarketer, considering that “any company with enough money to buy Chrome is already under scrutiny by antitrust authorities.”
However, the analyst considered that the U.S. government could authorize the sale to a domestic group to “prioritize innovation in artificial intelligence [AI] and position the U.S. globally in this technology.”
Would it benefit other browsers?
Analysts agree that people will continue using Chrome no matter its owner, as long as the quality doesn’t decrease. “This assumes that Chrome keeps its most popular features and continues innovating,” Mitchell-Wolf said.
“Search behaviors are a convenience function first, and trust and experience second,” she added.
The Department of Justice argues that people use Chrome because it is the default browser on their devices, and that if they had other options, they would use them, but analysts consider this “unlikely.”
The Trump Factor:
Many doubt that Judge Mehta will accept all of the Department of Justice’s recommendations. Analyst Angelo Zino from CFRA called the suggested measures extreme and unlikely to be imposed by the Court.
The incoming Trump administration also “remains an unpredictable factor,” regardless of whether the justice authorities take up the case.
In October, Trump said he opposed dismantling Google, considering that such a decision could go against U.S. interests internationally. “China is afraid of Google,” and the decision could harm the company, Trump estimated. However, the elected president has also accused Google of being unfair to conservative content.
by Future Labs Research Group Selection | Nov 6, 2024 | Artificial intelligence
Geoffrey Hinton has changed our lives. He made it possible for machines to learn on their own, and thus we have word predictors, image recognition, virtual assistants… and ChatGPT. When he received the BBVA Foundation Frontiers of Knowledge Award in 2017, we spoke with him in Toronto, and what he shared, which we revisit here, is especially revealing now that he himself admits his concern about the potential of artificial intelligence and the urgent need to control it. Back then, he didn’t see it as threatening…
Note: Hinton received the Nobel Prize in Physics in October 2024.
Geoffrey Hinton has not sat down since 2005. Literally. A serious back problem forces him to stand or lie down. He just smiles and shrugs at the challenge — “I’m used to it; you can get used to anything” — but it means this interview takes place standing up in his rundown office at the University of Toronto, in Canada. Hinton is now a prominent figure at Google, which hired him in 2013 to develop artificial intelligence, and he could have a better office, but this is where he’s worked since the eighties and has no interest in changing it.
Born in London in 1947, Hinton is called the ‘godfather’ of artificial intelligence. And it’s no casual title. The paternity of AI is highly disputed, but there’s no doubt about the godfather. His contribution is decisive. Against all logic, he chose to downplay logic in the creation of artificial intelligence. And he did so in 1972.
From a young age, Hinton has been truly interested in the brain, so his first field of study was Experimental Psychology at Cambridge. Once immersed in the keys to our mind, he decided he could replicate them and bring them to computing. He was inspired by biology to program and created what has come to be known as ‘neural networks.’ For 30 years, his proposal had no resonance within the scientific community. But as computer power increased, neural artificial intelligence started to perform better than that based solely on data accumulation. Hinton’s generative AI involves the system improving on its own as it learns (the algorithm weighs the most effective learning and modifies the strength of connections between the ‘neurons’ or nodes).
Suddenly, Hinton was a genius, a visionary. And Silicon Valley turned its eyes to him. Virtual assistants, simultaneous translators, image recognition, word predictors, driverless cars… Behind all of these is one brain: Geoffrey Hinton’s.
XLSemanal: You proposed that machines work like the human brain, and even though we still don’t fully understand how our mind works, it turns out it works… Yours is quite the achievement.
Geoffrey Hinton: We don’t know how the brain works in depth, but we do know that when it learns something, it changes the strength of connections between neurons. And we know more or less how a neuron works. So, we’ve created a computer model applying the principles of a neuron and designed a learning algorithm so that the system improves as it learns.
The Unleashed AI
Hinton comes from a British family of scientists. His grandfather was the mathematician George Boole, who laid the foundations of computational arithmetic, and his father was the renowned entomologist H. E. Hinton. He has spent 40 years working on generative artificial intelligence, when no one else believed in it. Now, with ChatGPT representing a substantial leap in that direction, Hinton has expressed his fears that a reckless step has been taken and that AI may escape human control much sooner than we think. At 75, he has left Google and moved to London, from where he wants to do everything possible to prevent the dark side of his creation from taking over.
XL: This is going to require a more detailed explanation, but it seems that artificial intelligence will not only create smarter machines, but it might also make us smarter…
G.H.: Maybe we won’t be smarter, but we will understand our brain better.
XL: But if we don’t improve our brain capabilities and machines do, they’ll end up dominating us.
G.H.: No, I don’t think so. There will be a symbiosis. Computers with neural network simulators and people will work together. I don’t think we’ll end up dominated by machines, and if that happens, it will be in a very, very distant future.
XL: Well, at the Center for Existential Risk at the University of Cambridge, created by people working on artificial intelligence, they’re not so sure. They’re studying the possibility that it could happen in 50 years.
G.H.: 50 years? It’s impossible to know what will happen in 50 years! I know the center, but you can’t make serious predictions beyond 5 years.
“Robots won’t dominate us. A less intelligent system can control a superior one. Look at babies. The mother can’t stand their crying. Something less powerful than her dominates her.”
XL: Let’s assume that possibility exists… What could we do to prevent it? Introduce a system of values into machines or create an ethical algorithm, as some suggest?
G.H.: We already have an example of a less intelligent system controlling a more intelligent one. And that’s a baby. A mother simply cannot stand her baby’s crying. She’s designed not to be able to remain indifferent. It’s an example of how something you would think is more powerful — the mother — has something inside her, built by evolution, that allows something less powerful — the baby — to control her and prevents her from abandoning it or throwing it out the window. Babies have found a way to control mothers.
XL: What you mean, I understand, is that we are the babies, and the superintelligent machines are the mothers, and despite that, we will be able to control them, is that right?
G.H.: That’s right. We will build this ‘thing’ into the mothers, that is, the machines, that they cannot resist, and it will make them turn off.
XL: Are we going to start crying?
G.H.: I don’t know what we’ll do. But we already have an example that it’s possible. If evolution did it with mothers, we’ll be able to do it with machines.
“The solution for those who lose their jobs to robots is universal basic income. Progress cannot be stopped”:
XL: You’ve said that the responsibility for what machines do is not the scientists’ but the politicians’.
G.H.: There are two different issues here. One is that machines become more intelligent than us and surpass us. If that happens, it will be in the very distant future. Another issue is that machines are so intelligent they can perform many of today’s jobs, leaving people unemployed. This issue falls to the political system. If machines can do things like dispense money in a bank, it’s intrinsically more efficient, so it should be intrinsically good for people. And what we want is for it to be better for all people, not just a few. That’s a political matter, and politicians need to solve it.
XL: Any ideas?
G.H.: Universal basic income. I’m in favor of that, for example. In fact, I think it’s the only solution. Because what’s certain is that you can’t stop progress. There’s no way to avoid ATMs. And nobody thinks they were a bad idea. The solution is to change the political system so that when more wealth is created because machines are more efficient, that wealth is shared.
XL: One of the fields where your neural networks are most effective is translation. It seems that soon we won’t need to learn languages, right?
G.H.: We’re still a bit far from perfect translations, but they’re already quite good. It will be like calculators. Nobody bothers with mental math anymore. For everyday work and business transactions, you won’t need to learn the language. However, for quality translations, you’ll still need people. And if you want to understand a culture, you’ll have to learn its language.
XL: Another use where your algorithms are very useful is in stock market predictions, meaning speculation. How will this change the financial world?
G.H.: Well, that’s already happening. There’s a lot of automatic trading, and some people are making a fortune with it.
XL: Could this unleashed speculation, based on the speed of data processing, lead us to another crisis?
G.H.: I don’t know, I’m not an expert in that area. But what I believe is that all of that should be highly regulated. The danger isn’t the machines, whether they go fast or not, the danger is that regulations are being removed.
Mechanical Intuition
25 years ago, the computer Deep Blue defeated chess champion Gary Kasparov. The next challenge was to beat Go, a Chinese game that’s difficult for a computer because it requires intuition. In 2016, Google created AlphaGo, based on Hinton’s AI. And the machine defeated the human again. It did so with ‘incomprehensible’ moves, meaning it was being creative.
XL: The great advantage of neural networks is that they are intuitive, in addition to logical. But what role do feelings play in all of this? Can a machine fall in love?
G.H.: Yes.
XL: Yes?
G.H.: Humans are machines, just very, very sophisticated ones.
XL: That statement would require a deeper explanation, but I was referring to those that are not made of flesh and bone. Can a robot fall in love?
G.H.: Of course it could. Imagine your brain. And imagine we replace each brain cell with a machine that works exactly the same as that cell. Imagine we could do that with nanotechnology. Then I replace all your brain’s neurons with tiny machines that act exactly like your neurons. Whatever you did before, this new system will do it now. If you laughed at a joke, this new system will laugh too; if you’re offended by someone’s behavior, this new system will be offended too, it will have feelings…
XL: But reproducing that isn’t possible.
G.H.: It’s not possible… today. But it is possible. The thing is, people don’t understand what it means to have feelings. It’s a philosophical problem.
XL: Well, the way you describe it doesn’t seem philosophical, but rather mechanical. Replacing neurons with chips…
G.H.: It’s philosophical. If you ask a person to explain their feelings, they’ll say something like, “I feel like I want to hit someone.” They translate it into actions they could take in the real world or talk about the causes. So when people talk about feelings, they’re not talking about something inside the head, they’re not referring to neuronal activity. Because we’re not used to it. If I say, “Your neuron 52 is very active,” that won’t mean anything to you, but if you feel like hitting someone, it’s because neuron 52 is very active. So ‘feelings’ is just a silly language to talk about states of the brain.
XL: So, we don’t understand feelings…
G.H.: We always talk in terms of their causes or effects. Not what happens in the brain.
XL: And how do we transfer those feelings to machines?
G.H.: We have a machine to which we can give inputs, and it’s capable of suppressing its actions, it can inhibit itself from acting. Normally, that machine behaves in a certain way when we give it those inputs, but now we tell the robot: “I want you not to do it, but I want you to tell me what you would do if you could do something based on those inputs.” And the machine would say: “If I could, I would move that piece.” That is, the robot feels like it wants to move a piece. The robot has a feeling. Even though it doesn’t do it. And that’s how a feeling works.
XL: And now we have a philosophical question… If you are only what your neurons are, and they come ‘pre-programmed’ when you’re born, are you responsible for your actions?
G.H.: Of course. There is no conflict between determinism and responsibility. Although that would take us to another topic. But having certain neurons in no way eliminates our responsibility for who we are and what we do.
XL: I suspect you’re not a religious person…
G.H.: That’s an accurate suspicion.
XL: You dedicated 40 years to approaching artificial intelligence as if it were the human brain without receiving any recognition. What do you say now to those who criticized you for wasting your time?
G.H.: What I tell them is that there should be better theories than the ones we use today. Many people have stopped searching for better ways of neural networks because these are working, and that’s a big mistake. The moment you’re satisfied, you’re not going anywhere.
XL: Is that why someone like you has started working for Google?
G.H.: What I do at Google is try to develop new types of neural networks, more efficient ones. They have faster computers and allow me to spend all my time researching.
XL: When you were young, you refused to work for the U.S. Army, which was very interested in your research and willing to fund it. Does working for Google not make you uneasy?
G.H.: I wouldn’t work for Google if they developed weapons.
XL: But they do business, and not always transparently…
G.H.: They make the economy more efficient.
“I don’t think we’ll end up being ruled by Silicon Valley. We will always need political leaders. If you want to change the world, study social sciences.”
XL: But we don’t know what they do with our data, if they sell it for others to sell us products… Isn’t all of that unclear, don’t you think?
G.H.: Google is very careful with what it does with personal data. And, of course, I trust Google much more than the NSA. Google was horrified when it realized the NSA was intercepting its servers. Genuinely horrified. I was there.
XL: Maybe we’ll end up having to trust the guys in Silicon Valley more than the politicians, especially in times of Trump…
G.H.: I’m not going to make any comments on Trump.
XL: Fine. But do you think we’ll end up being governed by Silicon Valley and the algorithm elite?
G.H.: No, I don’t think so. We will always need political leaders. The thing is, people like to think that when things go wrong, it’s because of political leaders, instead of thinking in terms of systems. It’s the social system and its dynamics that we should understand and organize to make it work well.
XL: What career should we study today to get a job?
G.H.: If you study neural networks, you’ll definitely find a job right now. But if you want to change the world, study social sciences.
Highlighted Opinions – Geoffrey Hinton
Article by Ana Tagarro – ABC Madrid. Published on May 8, 2023, updated by the author on September 27, 2024.
Ana Tagarro is a prominent figure in the field of research and education, especially known for her work on topics related to science and technology. She has contributed to scientific dissemination and has been involved in projects that aim to bring science closer to society.
