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Future Technology: Artificial Intelligence, Robotics, IoT and Big Data
Augmented reality, Cryptocurrency, Exocortex, Machine vision, Quantum computing, Speech recognition, Virtual Reality
Note: This is a blog for future upcomming most important technology like IOT(Internet of things),Big data and Robot technology
In today's world, robotics has become a major player in technology and innovation. From manufacturing to healthcare to space exploration, robotics is changing the way we work, live, and explore the world around us. In this blog post, we'll explore the world of robotics and how it's shaping the future.
What is Robotics?
Robotics is a field of engineering that deals with the design, construction, and operation of robots. Robots are machines that are capable of carrying out complex tasks autonomously or with minimal human intervention. They can be programmed to perform a variety of functions, from manufacturing and assembly to cleaning and maintenance.
Types of Robots
There are many different types of robots, each with its own specific functions and capabilities. Some of the most common types of robots include:
Industrial Robots - These are robots that are used in manufacturing and production facilities. They are designed to perform repetitive tasks, such as assembly or welding, and can operate around the clock.
Medical Robots - These are robots that are used in healthcare settings. They can perform delicate surgeries or assist with rehabilitation and therapy.
Service Robots - These are robots that are designed to assist people with everyday tasks, such as cleaning or cooking. They can also be used in hospitality and customer service industries.
Space Robots - These are robots that are designed to explore space and other planets. They are used to collect data and samples, and can operate in harsh environments where human exploration is not possible.
Advancements in Robotics
Over the years, there have been many advancements in robotics technology. One of the biggest breakthroughs has been the development of artificial intelligence (AI). AI allows robots to learn and adapt to new situations, making them more versatile and efficient.
Another major advancement has been the use of sensors and cameras to allow robots to interact with their environment. This has led to the development of autonomous robots that can operate independently without human intervention.
Impact of Robotics
Robotics has had a significant impact on many different industries. In manufacturing, robots have increased efficiency and reduced costs. In healthcare, robots have assisted with surgeries and rehabilitation, improving patient outcomes. In space exploration, robots have collected data and samples, expanding our knowledge of the universe.
However, there are also concerns about the impact of robotics on the workforce. As more tasks become automated, there may be job losses in certain industries. It's important to address these concerns and find ways to ensure that everyone benefits from advancements in robotics technology.
Conclusion
Robotics is an exciting field that is shaping the future of technology and innovation. As robotics technology continues to advance, we can expect to see more sophisticated robots that are capable of performing even more complex tasks. While there are concerns about the impact of robotics on the workforce, there are also opportunities to create new jobs and industries. Overall, the future of robotics is bright, and it's an exciting time to be part of this rapidly evolving field.
Transportation is a crucial part of our daily lives, and technology is already transforming the way we move around. In the coming years, we can expect even more changes that will revolutionize the way we travel. Here are just a few of the ways technology will shape the future of transportation:
Electric and Autonomous Vehicles
Electric vehicles (EVs) are becoming increasingly popular, and many countries have set ambitious targets to phase out gas-powered cars. At the same time, autonomous vehicles (AVs) are being developed that could eventually eliminate the need for drivers altogether. Combined, these technologies have the potential to make transportation safer, more efficient, and more sustainable.
Hyperloop and Other High-Speed Trains
Hyperloop is a proposed mode of transportation that would use vacuum-sealed tubes to transport passengers and cargo at high speeds. The technology is still in development, but it could revolutionize long-distance travel by significantly reducing travel times. Other high-speed trains, such as the Shanghai Maglev and the Japanese Shinkansen, are already in operation and could become even faster and more efficient in the future.
Flying Cars and Personal Air Transportation
Flying cars have been a staple of science fiction for decades, but they're closer to becoming a reality than ever before. Companies like Uber and Airbus are developing vertical takeoff and landing (VTOL) vehicles that could transport people and goods through the air. These vehicles could reduce congestion on the ground and provide a new mode of transportation for those who live in hard-to-reach areas.
Smart Infrastructure
Smart infrastructure refers to the integration of technology into our transportation systems, such as sensors and data analytics. This can improve traffic flow, reduce accidents, and enhance the overall efficiency of transportation networks. For example, smart traffic lights can adjust their timing based on real-time traffic data, while connected vehicles can communicate with each other to prevent collisions.
Space Travel
Space travel is no longer the exclusive domain of astronauts and billionaires. Companies like SpaceX and Blue Origin are developing spacecraft that could eventually make space tourism a reality. In the future, it's possible that space travel could become a more accessible and affordable mode of transportation.
The future of transportation is exciting and full of possibilities. With the help of technology, we can create transportation systems that are safer, more efficient, and more sustainable. Whether it's electric and autonomous vehicles, hyperloop and high-speed trains, flying cars and personal air transportation, smart infrastructure, or space travel, the future is full of possibilities.
Quick quiz: what do you visualize when you hear the word robot? Some machine in human form, behaving like a human? Or perhaps one of the massive, spiderlike machines that assemble complex products ranging from automobiles to computers?
In reality, modern robotics follows both paths. However, in spite of the progress with humanlike robots, most business applications of the technology focus on machine forms that are specific to the task at hand, whether it be a Mars Rover or a medical robot assisting a surgeon.
There are exceptions to this rule, however: companies who focus on making robots as humanlike as possible, with the eventual goal of behavior so realistic it can actually fool people into thinking the machine is fully human.
To achieve such levels of verisimilitude, such robots must have sophisticated artificial intelligence (AI) built in. For example, take Sophia, the creation of Hanson Robotics that Saudi Arabia recently granted citizenship to, in a lavish publicity stunt.
Sophia has the ability to carry on limited conversations, requiring AI-driven natural language processing. ‘She’ can also recognize faces and respond to interactions emotionally, both via tone of voice as well as through a complex set of facial expressions – all technologies that depend upon AI.
In many ways, however, Sophia is less about AI and more about theatrics. ‘Her’ creators have combined AI, robotics, and elements of show business to prove that a fake person can be sufficiently realistic to be appealing rather than repulsive. Not In Our Digital House
What they have not accomplished – and in fact, aren’t attempting to accomplish – is to create AI that can simulate human behavior in a way that solves true business problems.
Sophia the Robot at Web Summit 2017STEPHEN MCCARTHY/WEB SUMMIT.
What Do We Really Want from Human-Like AI?
In spite of decades of Hollywood influence, the real money in robotics today is in the massive armlike devices from industry. No witty banter, no subtle smiles here – instead, these machines focus on the tasks at hand.
When we talk about AI, however, we’re on shakier ground, as most of today’s applications of AI in business are not particularly humanlike. True, we have voice interactions like Siri, image recognition, and machine learning-driven predictions based upon crunching massive quantities of data – but if you think about it, these capabilities are only vaguely humanlike at best.
On the other hand, Artificial General Intelligence (AGI) – that is, true humanlike reasoning ability – is still well out of reach today. In the absence of AGI, then, what are the most advanced AI capabilities we can currently bring to bear to solve real business problems?
I recently spoke with Donald Thompson, Founder and CTO of AI-driven knowledge platform vendor Maana, to get his take on this question. “Maana supports decision making, reasoning, and answering questions,” Thompson explained. “We construct models that represent human expertise and combine them with data models in the context of optimizing a decision flow or equipment like an oil well.”
Where Sophia is able to mimic human expressions and conversations, Maana’s ability to mimic – or perhaps duplicate – human expertise provides the business value context that Sophia’s theatrics cannot.
The essence of Maana’s technology is what Thompson calls ‘digital knowledge.’ “Digital knowledge consists of models of the domain of business artifacts with the business goal in mind. These models digitize decision flows, and provide recommendations that help experts in the organization make better and faster decisions,” he explained.
The business artifacts in question could represent a wide variety of different types of information, from PDF documents to spreadsheets to emails to sensor data to information bottled up in various applications. From these artifacts, Maana is able to create data models, as well as computational models that mathematically model the human knowledge of a specific business process.
Given the rather philosophical questions about the nature of human knowledge, Maana puts a fine point on this conundrum. “We phrase every knowledge model as an answer to a business question,” Thompson said. “These are higher order questions, for example, ‘given the pump failure in a particular oil well, what are other oil wells with similar pumps?’”
The choice of oil wells in this example is no accident: Maana has found traction in the petroleum industry, listing companies such as Chevron CVX +0.66% Corporation, Royal Dutch Shell , and Saudi Aramco as customers as well as investors.
The reason that the oil business has taken to Maana’s AI-driven decision-making capability is due to the combination of the industry’s massive big data sets with its ability to distil its business goals into easily digested statements.
Thompson clarified the types of goals Maana’s technology targets. “The goals include minimization (for example, risks or costs) or maximization (for example, profits),” he said. “For example, given this list of constraints, what risks should we mitigate?”
In fact, Maana’s ability to interpret constraints gives the technology a leg up on modeling human expertise, as such expertise often boils down to dealing with constraints on a day-to-day basis.
For example, a person may know how to accomplish a goal, but also must know about all the various exceptions that might occur and how to deal with them. Such exceptions are a type of constraint.
Knowledge modeling in order to achieve certain goals can be thought of as decision support. “By decision support we mean that the technology can observe a particular domain, reason about the domain, justify and explain its reasoning, and then allow the user to hypothesize about different courses of action.”
The latter point is especially important: the technology does not generally make business decisions itself, but rather supports human decision making – in part by empowering people to simulate the results of different actions they may take.
Such decision support requires a mix of technical capabilities. “It’s a combination of natural language processing, constraint satisfaction, and simulation,” Thompson explained. “Maana adds constraint satisfaction and Bayesian probabilistic inference.”
The result is human-like reasoning, as different from simpler forms of AI like machine learning as the auto assembly robot is from Sophia. “Contrast Maana’s approach with machine learning, which is how to train machines to learn from data,” Thompson said. “Reasoning is the ability to answer a question.”
As with every vendor in the emerging AI marketplace, Maana’s technology will continue to improve over time. However, its ability to model human expertise to answer business questions in order to support mission-critical decisions gives the technology human-like characteristics that provide business value that other technologies cannot, the gimmicks and other theatrics of Sophia notwithstanding.
Intellyx publishes the Agile Digital Transformation Roadmap poster, advises companies on their digital transformation initiatives, and helps vendors communicate their agility stories. As of the time of writing, Maana is an Intellyx customer. None of the other organizations mentioned in this article are Intellyx customers. Image credit: Stephen McCarthy/Web Summit.
Jason Bloomberg is president of industry analyst firm Intellyx.
Jason Bloomberg is a leading IT industry analyst, Forbes contributor, keynote speaker, and globally recognized expert on multiple disruptive trends in enterprise technology and digital transformation. He is founder and president of Agile Digital Transformation analy...
Artificial Intelligence can compose songs, paint pictures, help in cancer therapy, drive cars and play games. It’s also starting to write code.
Does it mean that the days of human developers are already numbered?
When you look at tests of personal assistants such as Siri, Google Assistant or Cortana, you will discover that probably the most frequent request they hear is:
Tell me a joke.
Is that really all that Artificial Intelligence can do for us? Not at all. Its achievements are getting more impressive every day.
So before we tackle the question of Artificial Intelligence replacing software devs, let’s explore the achievements of AI thus far.
What can AI do?
Beat humans in board games and quizzes
In 1997 in New York City, the IBM computer Deep Blue won a chess match against Garry Kasparov. It was the first time when a machine defeated the world chess champion under tournament conditions.
In 2011, another IBM computer - Watson - took part in the television quiz show “Jeopardy” against former winners. Watson had to listen to questions and give answers in natural human language.
He was not connected to the internet.
However, he learned from 200 million pages of structured and unstructured content taking up four terabytes of disk storage. Watson won the first place prize of $1 million.
In March 2016, AlphaGo - a computer programme from Google DeepMind created to play the board game Go - beat Lee Sedol, the World Champion in Go. The man and the machine played a five-game tournament in Seoul. Lee Sedol won only the fourth game. In the rest, the machine proved superior.
Lee Sedol playing against DeepMind - an Artificial Intelligence from Google
Make medical discoveries
AI has already led to breakthroughs in medical diagnostics.
In 2013, Artificial Intelligence was put to work to detect breast cancer. A neural network was trained to find signs of cancer using tens of thousands of mammographic pictures of the disease.
But the neural network has learned that it is not so important to look for the tumors themselves, but rather some other modifications of the tissue which aren’t in the immediate vicinity of the tumor cells.
This was new knowledge to humankind. Until 2013, medicine didn’t know that.
Compose songs
Magenta is a project from the Google Brain team. It tries to answer the question: “Can we use machine learning to create compelling art and music? If so, how? If not, why not?”
The team works using TensorFlow - a machine learning library from Google. Have a listen to Magenta’s first computer-generated song - composed without any human assistance.
Paint pictures
In February 2016 in San Francisco, Google sold 29 paintings on a charity auction. All of them were made by Google’s Artificial Intelligence.
The event was called “DeepDream: The art of neural networks.” The paintings (masterpieces, if you ask me) went for more than 8000$, as The Wall Street Journal reports.
Google Deep Dream painting
And that’s not all AI can do. It can also drive motorways, write poems, and much more.
What it can’t do is write code. Or can it?
AI for everyone
In December 2015, Google released the TensorFlow library to the public. Now it’s an open-source software for machine learning.
Why did Google give out this powerful piece of software for free? According to prof. Christian Bauckhage from Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme, Germany (IAIS), you can find the answer in Google’s history. About 10 years ago, Google has open sourced the Android Operating System for smartphones. 10 years later, 80% of all the smartphones in the world run on Android.
"This is what they are trying to do right now. 10 years from now, the idea is that 80% of AI will run on Google TensorFlow," prof. Bauckhage said at the CeBIT Conference last year.
What happened after Google’s release? A few weeks later Microsoft open-sourced their Computational Network Toolkit AI - a deep learning framework now called the Microsoft Cognitive Toolkit.
After another few weeks, Facebook opened-sourced their own Artificial Intelligence libraries - Caffe2.
AI is writing code
In 2015 Andrej Karpathy, now director of AI at Tesla and a Stanford Computer Science Ph.D. student, used Recurrent Neural Networks to generate code. He took a Linux repository (all the source files and headers files), combined it into one giant document (it was more than 400 MB of code) and trained the RNN with this code.
He left it running for the night. In the morning, he got this:
Sample code generated by Artificial Intelligence
Literally overnight, the AI generated code including functions and function decorations. It had parameters, variables, loops and correct indents. Brackets were opened and later closed. It even had comments.
The AI made some mistakes of course. Sometimes variables were never used. Other times, there were variables which were not declared earlier. But Karpathy was satisfied with the result.
‘The code looks really quite great overall. Of course, I don’t think it compiles but when you scroll through the generate code it feels very much like a giant C code base,’ Karpathy wrote on his blog.
The project is available on GitHub. It uses the Torch7 deep learning library. Here is the whole output file received by Karpathy.
DeepCoder
Microsoft and Cambridge University researchers have developed an Artificial Intelligence that can write code. The AI is called DeepCoder and it has the ability to learn.
DeepCoder can write working code after searching through a huge code database. It tries to make the best possible arrangement for the harvested code fragments and improves its efficiency over time.
This doesn’t mean the AI steals code, or copy-pastes it from existing software, or searches the internet for solutions. The authors of DeepCoder expect that it will be participating programming competitions in the near future .
Example programme in Domain Specific Language (DSL) created by DeepCoder
According to Marc Brockschmidt of Microsoft Research, who is a part of the project, such system could be of great utility to non-coders. They only have to describe their program idea and wait for the system to create it.
‘We might end up having such system in the next few years. But for now, DeepCoder’s capabilities are limited to programs consisting of five lines of code,’ he said.
Since this is a primarily Python-focused blog, we would be remiss if we didn’t give you at least one Python example.
In June 2016, a French engineer by the nickname of BenjaminTD published a blog post in which he explained how he was “teaching an AI to write Python code with Python code”.
He used Long Short Term Memory (LSTM) - one of the most popular architectures of recurrent neural networks. He fed it with lots of Python code (using libraries such as Pandas, Numpy, Scipy, Django, Scikit-Learn, PyBrain, Lasagne, Rasterio). The combined file weighed 27MB.
The AI then generated its own code. It was defining inits:
...using boolean expressions:
...and creating arrays:
If you look at the arrays carefully, you will find a syntax error. Benjamin’s code is far from perfect. But the engineer thinks that it’s not bad for a network that had to learn everything from reading example code.
‘Especially considering that it is only trying to guess what is coming next character by character,’ he concludes his blog post.
Will AI replace programmers?
The Hollywood fiction of AI supplanting humans hasn’t come true yet. We are far from a 2001: A Space Odyssey scenarios of a rogue AI turning against its human masters and killing off space crews.
That does not stop filmmakers from generously employing the theme of AI rebellion in their works.
But can we be so sure that real-life AI can be controlled?
In 2016, Microsoft released a Twitter bot - Tay. It was designed to mimic the language patterns of a 19-year-old American girl, and to learn from interacting with human users of Twitter. After just 16 hours following its launch, Microsoft was forced to shut Tay down because the bot began to post offensive tweets.
That’s not the only AI issue on record. In early 2017, Facebook had to shut down its bots - Bob and Alice. They were created to perform conversations between human and computer. But when the bots were directed to talk with each other, they started to communicate in a way that was impossible for people to understand.
A few months later a Chinese chatbot - Baby Q - was switched off after it started to criticize the Chinese Communist Party. Baby Q called it "a corrupt and incompetent political regime".
So, is AI a threat or an opportunity? Elon Musk is known for his scepticism towards AI. His worry is what will happen when the machine becomes smarter than the human.
"Even in the benign scenario, if AI is much smarter than a person, what do we do? What job do we have?’" he asks.
There is no doubt that computers will be much better at programming in the near future than they are now. Which brings us to a quite scary conclusion.
"It’s just a matter of time until neural networks will produce useful code. So things are looking bleak for computer scientists like me" prof. Bauckhage believes.
But is the future really that dark? According to Armando Solar-Lezama of MIT, tools like DeepCoder do have the potential to automate code development, but AI isn’t going to take away the jobs of developers. Instead, a system based on program synthesis can be used to automate the tedious parts of code development while the developers focus on complex tasks.
There are already startups using automation to build ‘smart software’. Dev9 is a custom software development company based in Seattle focused on Java and JavaScript. Dev9 assembles teams that use artificial intelligence to develop custom software, eliminating strenuous processes and drastically reducing manual overhead. Will Iverson, its Chief Technology Officer, was asked if programmers need to be worried about being replaced by AI in the near future, and replied:
Eventually, yes. But by that point, society will be very used to dealing with that kind of societal change. The millions of paid drivers replaced by self-driving cars will have long since forced our political and economic systems to figure out how to deal with these transitions. We have joked around the office that software development will be one of the last professions left.
Will Iverson, CTO at Dev9
Regardless of whether our worries are justified, the fact is that nearly a third of software developers fear that artificial intelligence will eventually take their jobs. In an Evans Data Corp.survey, 550 software programmers were asked about the most worrisome thing in their careers. The most common response (29%) was:
"I and my development efforts are replaced by artificial intelligence."
According to Janel Garvin, CEO of Evans Data, the thought of obsolescence due to A.I., "was also more threatening than becoming old without a pension, being stifled at work by bad management, or by seeing their skills and tools become irrelevant."
There is no doubt that AI technology will developing and grow smarter. Eventually it will become smarter than humans. How can we handle such a possibility? Stephen Hawking also sees a real danger that computers will develop intelligence. But he also offers advice:
"We urgently need to develop direct connections to the brain so that computers can add to human intelligence rather than be in opposition," Hawking says.
The industrial robotics market is expected to grow from USD 38.11 Billion in 2016 to USD 71.72 Billion by 2023, at a CAGR of 9.60% during the forecast period. The main objective of the report is to forecast the industrial robotics market size in terms of value and volume for traditional industrial robots and collaborative robots. Further, it includes the detailed information regarding the drivers of the industrial robotics market, such as increase in investments for automation in industries and growing demand from small and medium-scale enterprises in developing countries. It also includes detailed information about restraints, opportunities, and challenges for the industrial robotics market. The study of the value chain of the industrial robotics market is also one of the objectives of the report, which includes information about suppliers and integrators in the value chain of the industrial robotics market.
Major players in the industrial robotics market ecosystem are identified across regions, and their offerings, distribution channels, and regional presence are understood through in-depth discussions. Also, average revenue generated by these companies, segmented by region, is used to arrive at the overall industrial robotics market size. This overall market size is used in the top-down procedure to estimate the sizes of other individual markets through percentage splits from secondary sources directories, databases (such as Hoovers, Bloomberg Businessweek, Factiva, and OneSource), and primary research. The entire procedure includes the study of annual and financial reports of the top market players and extensive interviews with industry experts such as CEOs, VPs, directors, and marketing executives for key insights.
The industrial robotics market ecosystem includes traditional industrial robot and collaborative robot manufacturers such as ABB Ltd. (Switzerland), KUKA AG (Germany), Mitsubishi Electric Corp. (Japan), FANUC Corporation (Japan), Kawasaki Heavy Industries Ltd. (Japan), Yaskawa Electric Corporation (Japan), Seiko Epson Corporation (Japan), Stäubli International AG (Switzerland), NACHI-FUJIKOSHI CORP. (Japan), DENSO CORPORATION (Japan), Comau SpA (Italy), DAIHEN Corporation (Japan), Omron Adept Technologies, Inc. (US), Universal Robots A/S (Denmark), and CMA ROBOTICS SPA (Italy), among others. The ecosystem also includes system integrators such as Dürr AG (Germany) and Artech Automation AS (Norway).
Key Target Audience:
Original equipment manufacturers (OEMs)
OEM technology solution providers
Research institutes
Market research and consulting firms
Forums, alliances, and associations
Technology investors
Governments and financial institutions
Analysts and strategic business planners
End users who want to know more about the technology and the latest technological developments in the industry
The study answers several questions for the stakeholders, primarily which market segments to focus on in the next 2–5 years (depends on the range of forecast period) for prioritizing efforts and investments.
Report Scope:
In this report, the industrial robotics market has been segmented into the following categories:
Market, by Type:
Traditional Industrial Robots
Articulated Robots
SCARA Robots
Parallel Robots
Cartesian Robots
Others
Collaborative Robots
Market, by Industry:
Automotive
Electrical and Electronics
Plastics, Rubber, and Chemicals
Metals and Machinery
Food and Beverages
Precision Engineering and Optics
Pharmaceuticals and Cosmetics
Others
Market, by Geography:
North America
US
Canada
Mexico
Europe
UK
Germany
France
Italy
Spain
Rest of Europe
APAC
China
Japan
Republic of Korea
Taiwan
Thailand
India
Rest of APAC
RoW
Middle East and Africa
South America
Competitive Landscape
Company Profiles: Detailed analysis of the major companies in the industrial robotics market
Available Customizations:
With the given market data, MarketsandMarkets offers customizations according to the company’s specific needs. The following customization options are available for the report:
Product Analysis
Product matrix that gives a detailed comparison of product portfolio of each company.
Company Information
Detailed analysis and profiling of additional market players (up to 5).
