IBM has released a new API for its Quantum Experience program, which will enable developers to build interfaces between its cloud-based quantum computers and its classical equivalents.

According to Gartner’s most recent hype cycle, from August last year, quantum computing – the process of using quantum-mechanical phenomena, such as entanglement, to perform operations – will take more than 10 years to hit the mainstream.

IBM defines it thus. “While technologies that currently run on classical computers, such as Watson, can help find patterns and insights buried in vast amounts of existing data, quantum computers will deliver solutions to important problems where patterns cannot be seen because the data doesn’t exist and the possibilities that you need to explore to get to the answer are too enormous to ever be processed by classical computers,” the company notes.

Use cases of quantum computing could include improving cloud security through the application of quantum physics, greater modelling of financial data, and making machine learning and artificial intelligence more powerful, IBM added.

According to the Armonk giant, quantum computing is the next cab off the rank to be enhanced through cloud-based platforms after machine learning and blockchain, two technologies much further ahead in Gartner’s cycle.

“IBM has invested over decades to growing the field of quantum computing and we are committed to expanding access to quantum systems and their powerful capabilities for the science and business communities,” said Arvind Krishna, senior vice president of hybrid cloud and director for IBM Research in a statement.

“Following Watson and blockchain, we believe that quantum computing will provide the next powerful set of services delivered via the IBM Cloud platform, and promises to be the next major technology that has the potential to drive a new era of innovation across industries,” Krishna added.

IBM’s record of innovation continues to blaze ahead, with more than 8,000 US patents granted last year, well ahead of nearest competitor Samsung, according to figures released in January. Of that number, around a third were related to artificial intelligence, cognitive computing and cloud computing.

You can find out more here.

Picture credit: “Quantum Computer Interior”, by “IBM Research”, used under CC BY ND

We have seen a machine master the complex game of Go, previously thought to be one of the most difficult challenge of artificial processing. We have witnessed vehicles operating autonomously, including a caravan of trucks crossing Europe with only a single operator to monitor systems. We have seen a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks. All of this has given rise to a flurry of people claiming that the AI revolution is already upon us.

Understanding the growth in the functional and technological capability of AI is crucial for understanding the real world advances we have seen. Full AI, that is to say complete, autonomous sentience, involves the ability for a machine to mimic a human to the point that it would be indistinguishable from them (the so-called Turing test). This type of true AI remains a long way from reality. Some would say the major constraint to the future development of AI is no longer our ability to develop the necessary algorithms, but, rather, having the computing power to process the volume of data necessary to teach a machine to interpret complicated things like emotional responses. While it may be some time yet before we reach full AI, there will be many more practical applications of basic AI in the near term that hold the potential for significantly enhancing our lives.

With basic AI, the processing system, embedded within the appliance (local) or connected to a network (cloud), learns and interprets responses based on “experience.” That experience comes in the form of training through using data sets that simulate the situations we want the system to learn from. This is the confluence of machine learning (ML) and AI. The capability to teach machines to interpret data is the key underpinning technology that will enable more complex forms of AI that can be autonomous in their responses to input. It is this type of AI that is getting the most attention. In the next ten years, the use of this kind of ML-based AI will likely fall into two categories:

• Improvement and automation of daily life: Managing household tasks, self-driving cars and trucks and the general automation of tasks that robots can perform significantly faster and more reliably than humans;
• Exploration and development of new trends and insights: Artificial intelligence can help accelerate the rate discovery and science happening worldwide every day. The use of AI to automate science and technology will drive our ability to discover new cures, technologies, tools, cells, planets, etc., ultimately pushing artificial intelligence itself to new heights.

There is no doubt about the commercial prospects for autonomous robotic systems for applications like online sales conversion, customer satisfaction, and operational efficiency. We see this application already being advanced to the point that it will become commercially viable, which is the first step to it becoming practical and widespread. Simply put, if revenue can be made from it, it will become self-sustaining and thus continue to grow. The Amazon Echo, a personal assistant, has succeeded as a solidly commercial application of autonomous technology in the United States.

In addition to the automation of transportation and logistics, a wide variety of additional technologies that utilise autonomous processing techniques are being built. Currently, the artificial assistant or “chatbot” concept is one of the most popular. By creating the illusion of a fully sentient remote participant, it makes interaction with technology more approachable. There have been obvious failings of this technology (the unfiltered Microsoft chatbot, “Tay,” as a prime example), but the application of properly developed and managed artificial systems for interaction is an important step along the route to full AI. This is also a hugely important application of AI as it will bring technology to those who previously could not engage with technology completely for any number of physical or mental reasons. By making technology simpler and more human to interact with, you remove some of the barriers to its use that cause difficulty for people with various impairments.

The use of AI for development and discovery is just now beginning to gain traction, but over the next decade, this will become an area of significant investment and development. There are so many repetitive tasks involved in any scientific or research project that using robotic intelligence engines to manage and perfect the more complex and repetitive tasks would greatly increase the speed at which new breakthroughs could be uncovered.

We have seen a machine master the complex game of Go, previously thought to be one of the most difficult challenge of artificial processing. We have witnessed vehicles operating autonomously, including a caravan of trucks crossing Europe with only a single operator to monitor systems. We have seen a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks. All of this has given rise to a flurry of people claiming that the AI revolution is already upon us.

Understanding the growth in the functional and technological capability of AI is crucial for understanding the real world advances we have seen. Full AI, that is to say complete, autonomous sentience, involves the ability for a machine to mimic a human to the point that it would be indistinguishable from them (the so-called Turing test). This type of true AI remains a long way from reality. Some would say the major constraint to the future development of AI is no longer our ability to develop the necessary algorithms, but, rather, having the computing power to process the volume of data necessary to teach a machine to interpret complicated things like emotional responses. While it may be some time yet before we reach full AI, there will be many more practical applications of basic AI in the near term that hold the potential for significantly enhancing our lives.

With basic AI, the processing system, embedded within the appliance (local) or connected to a network (cloud), learns and interprets responses based on “experience.” That experience comes in the form of training through using data sets that simulate the situations we want the system to learn from. This is the confluence of machine learning (ML) and AI. The capability to teach machines to interpret data is the key underpinning technology that will enable more complex forms of AI that can be autonomous in their responses to input. It is this type of AI that is getting the most attention. In the next ten years, the use of this kind of ML-based AI will likely fall into two categories:

• Improvement and automation of daily life: Managing household tasks, self-driving cars and trucks and the general automation of tasks that robots can perform significantly faster and more reliably than humans;
• Exploration and development of new trends and insights: Artificial intelligence can help accelerate the rate discovery and science happening worldwide every day. The use of AI to automate science and technology will drive our ability to discover new cures, technologies, tools, cells, planets, etc., ultimately pushing artificial intelligence itself to new heights.

There is no doubt about the commercial prospects for autonomous robotic systems for applications like online sales conversion, customer satisfaction, and operational efficiency. We see this application already being advanced to the point that it will become commercially viable, which is the first step to it becoming practical and widespread. Simply put, if revenue can be made from it, it will become self-sustaining and thus continue to grow. The Amazon Echo, a personal assistant, has succeeded as a solidly commercial application of autonomous technology in the United States.

In addition to the automation of transportation and logistics, a wide variety of additional technologies that utilise autonomous processing techniques are being built. Currently, the artificial assistant or “chatbot” concept is one of the most popular. By creating the illusion of a fully sentient remote participant, it makes interaction with technology more approachable. There have been obvious failings of this technology (the unfiltered Microsoft chatbot, “Tay,” as a prime example), but the application of properly developed and managed artificial systems for interaction is an important step along the route to full AI. This is also a hugely important application of AI as it will bring technology to those who previously could not engage with technology completely for any number of physical or mental reasons. By making technology simpler and more human to interact with, you remove some of the barriers to its use that cause difficulty for people with various impairments.

The use of AI for development and discovery is just now beginning to gain traction, but over the next decade, this will become an area of significant investment and development. There are so many repetitive tasks involved in any scientific or research project that using robotic intelligence engines to manage and perfect the more complex and repetitive tasks would greatly increase the speed at which new breakthroughs could be uncovered.

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Ransomware attacks escalated dramatically in 2016. In fact, there was a 300 percent increase in ransomware attacks last year, according to the FBI, to an average of 4,000 attacks a day, up from 1,000 ransomware attacks a day in 2015. What’s more, organizations are targeted more frequently than individuals because they generate a much bigger potential payoff. Ransomware has become a profitable criminal enterprise that continues to change and grow.

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