Over the last few months, artificial intelligence (AI) has dominated headlines everywhere. And now, it looks like this new-age technology has taken over big tech companies, enterprises, startups, schools, and life itself. From music, art, and films to homework, news, and more, AI is the next big thing.

Suffice it to say, it is important to keep up with why AI is this interesting, what it is made up of, and why exactly it is a big deal.

In this article, we will navigate the vast landscape of artificial intelligence terminology across machine learning (ML), natural language processing (NLP), deep learning, quantum computing, and a lot more.

While numerous scientists and engineers laid the groundwork for AI since the 1940s, American Computer scientist John McCarthy coined the term Artificial Intelligence in 1955. A year later, he, along with other scientists, held the first AI conference at Dartmouth University.

In the 1980s, there was a shift towards neural networks and machine learning approaches. Researchers explored algorithms inspired by the structure and functioning of the human brain, enabling machines to learn from data and improve their performance over time.

The late 1980s and early 1990s witnessed a period known as the "AI Winter" when interest and funding significantly declined in this area due to unmet expectations. However, the field experienced a resurgence in the late 1990s with advancements in areas such as data mining, natural language processing, and computer vision.

In recent years, the availability of vast amounts of data and advancements in computational power have fuelled breakthroughs in AI. Deep learning, a subfield of machine learning that utilises neural networks with multiple layers, has led to significant advancements in image and speech recognition, natural language processing, and other AI applications.

An algorithm is a definite set of instructions that allow a computer to perform a certain task. AI algorithms help a computer understand how to perform certain tasks and achieve the desired results on its own. In other words, algorithms set the process for decision-making.

Machine learning is a subset of AI. It effectively enables machines to learn using algorithms, data and statistical models to make better decisions. While AI is a broad term that refers to the ability of computers to mimic human thought and behaviours, ML is an application of AI used to train computers to do specific tasks using data and pattern recognition.

A subset of ML, deep learning trains computers to do what humans canlearn by example. Computer models can be taught to perform tasks by recognising patterns in images, text, or sound, sometimes surpassing humans in their ability to make connections. Computer scientists leverage large sets of data and neural network architectures to teach computers to perform these tasks.

DL is employed in cutting-edge technology like driverless cars to process a stop sign or differentiate between a human and a lamp post.

Yet another application of ML, natural language processing helps machines understand, interpret, and process human language to perform routine tasks. It uses rules of linguistics, statistics, ML, and DL to equip computers to fully understand what a human is communicating through text or audio and perform relevant tasks. AI virtual assistants and AI voice recognition systems like voice-operated GPS are examples of NLP.

Computer vision is a form of AI that trains computers to recognise visual input. For instance, a machine will be able to analyse and interpret images, videos and other visual objects to perform certain tasks that are expected of it.

An example is medical professionals using this technology to scan MRIs, X-rays or ultrasounds to detect health problems in humans.

Robotics is a branch of engineering, computer science, and AI that designs machines to perform human-like tasks without human intervention. These robots can be used to perform a wide variety of tasks that are either too complex and difficult for humans or are repetitive or both. For example, building a robotic arm to assemble cars in an assembly line is an example of a robot.

Data science uses large sets of structured and unstructured data to generate insights that data scientists and others can use to make informed decisions. Often, data science employs ML practices to find solutions to different challenges and solve real-world problems.

For instance, financial institutions may employ data science to analyse a customers financial situation and bill-paying history to make better decisions on lending.

An extension of data science is data mining. It involves extracting useful and pertinent information from a large data set and providing valuable insights. It is also known as knowledge discovery in data (KDD). Data mining has numerous applications, including in sales and marketing, education, fraud detection, and improving operational efficiency.

Quantum computing uses theories of quantum physics to solve complex problems that classic computing cannot solve. It is used to run complex simulations in a matter of seconds by converting real-time language into quantum language.

Google has a quantum computer that they claim is 100 million times faster than an average computer. Quantum computing can be used in a variety of fields ranging from cybersecurity to pharmaceuticals to solve big problems with fewer resources.

A chatbot employs AI and NLP to simulate human conversations. It can operate through text or voice conversations. Chatbots use AI to analyse millions of conversations, learn from human responses and mimic them to provide human-like responses. This tech has found great usage in customer service and as AI virtual assistants.

All AI and ML tools are human-trained. This means that any inherent human bias can reflect AI bias. AI bias is a term that refers to the tendency of machines to adopt human biases because of how and by whom they are coded or trained. Algorithms can often reinforce human biases.

For instance, a facial recognition platform may be able to recognise Caucasian people better than people of colour because of the data set it has been fed. It is possible to reduce AI bias through more testing in real-life circumstances, accounting for these biases and improving how humans operating these systems are educated.

Today, AI represents the human capacity to create, innovate, and push the boundaries of what was once thought impossible.

So, whether you are an AI enthusiast, a curious learner, or a decision-maker shaping the future, it is essential to equip yourself with the right knowledge to survive in a world that is being increasingly powered by AI and its tools.

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AI 101: 10 artificial intelligence terms to keep up with this new-age ... - YourStory

Exploring the Synergy between Neuromorphic Computing and Quantum Computing for Advanced AI Applications

The rapid advancements in artificial intelligence (AI) and machine learning (ML) have created a significant demand for powerful computing systems that can handle the massive amounts of data and complex algorithms involved in these fields. Traditional computing architectures, such as those based on the von Neumann model, are reaching their limits in terms of energy efficiency and processing capabilities. This has led researchers to explore alternative computing paradigms, such as neuromorphic computing and quantum computing, which hold the potential to revolutionize the way we process and analyze information.

Neuromorphic computing is a novel approach that aims to mimic the structure and function of the human brain in order to create more efficient and adaptive computing systems. It is based on the idea of using artificial neural networks, which are composed of interconnected artificial neurons, to process and store information. These networks can be implemented in hardware, using specialized electronic components, or in software, running on conventional computing platforms. Neuromorphic systems are designed to be highly parallel, fault-tolerant, and energy-efficient, making them well-suited for AI and ML applications.

Quantum computing, on the other hand, is a fundamentally different approach that relies on the principles of quantum mechanics to perform computations. Unlike classical computers, which use bits to represent information as either 0 or 1, quantum computers use quantum bits, or qubits, which can exist in multiple states simultaneously due to a phenomenon known as superposition. This allows quantum computers to perform certain types of calculations much faster than classical computers, potentially enabling them to solve problems that are currently intractable.

The synergy between neuromorphic computing and quantum computing is an exciting area of research that could lead to the development of advanced AI applications that were previously thought to be impossible. By combining the strengths of both paradigms, researchers hope to create hybrid systems that can tackle complex problems in areas such as natural language processing, pattern recognition, and decision-making.

One of the key challenges in developing such hybrid systems is finding ways to integrate neuromorphic and quantum components in a seamless and efficient manner. Researchers are exploring various techniques to achieve this, such as using quantum-inspired algorithms to train neuromorphic networks, or employing neuromorphic hardware to control and read out the states of qubits in a quantum processor.

Another important aspect of this research is the development of new materials and fabrication techniques that can support the implementation of neuromorphic and quantum devices. For example, researchers are investigating the use of superconducting materials, which can carry electrical currents without resistance, to create energy-efficient neuromorphic circuits and qubits. They are also exploring the potential of nanoscale structures, such as quantum dots and nanowires, to enable the miniaturization and integration of these devices.

As the field of neuromorphic-quantum computing continues to evolve, it is expected to have a profound impact on the future of AI and ML. By harnessing the power of both neuromorphic and quantum computing, researchers aim to develop systems that can learn and adapt in real-time, allowing them to handle complex tasks with greater speed and accuracy than ever before. This could lead to breakthroughs in areas such as robotics, autonomous vehicles, and personalized medicine, among others.

In conclusion, the synergy between neuromorphic computing and quantum computing holds great promise for the future of AI and ML applications. By exploring the potential of these two emerging paradigms, researchers are paving the way for the development of advanced computing systems that can tackle some of the most challenging problems in science and technology. As we continue to push the boundaries of what is possible with AI and ML, the integration of neuromorphic and quantum computing will undoubtedly play a crucial role in shaping the future of these fields.

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The Role of Neuromorphic Computing in the Future of Quantum ... - CityLife

(Image by Garik Barseghyan from Pixabay )

The quantum tipping point that fabled moment when quantum technologies break through to commercial adoption at scale has beenquestioned in a previous diginomica report.

According to speakers at Davos this year, a more likely scenario is the gradual emergence of vertical use cases in which a quantum advantage finding hidden correlations between data points, for example will present itself. Plus, algorithms that allow the querying of massive data sets more quickly than classical models.

Even so, investors of every size are beginning to back quantum technologies, especially those that relate to enterprise data. And they are doing so despite the lack of strategic, long-term thinking among potential customers.

According to McKinsey, quantum investment exceeded $1.4 billion in 2021, while the UK government launched a 10-year, 2.5 billion ($3.2 billion) funding drive in March.

One of the challenges, says David Magerman, a founding partner at New York City-based investment house Differential Ventures, is that in a world of AI hype and a rush to associate with popular technologies like GPT, some enterprise users prefer a quick hit of tactical advantage to a long bet with an uncertain payback date.

A sugar rush trumps healthy eating for the future, perhaps? Magerman says:

I come from the financial industry. And while there are a lot of problems that quantum computing will solve significantly better [than classical systems], the financial industry is very much about the next five minutes.

All the spending that goes on research in finance is about solving problems today. There's such a competitive environment that they can't afford to be too forward-looking. We can't get the financial industry to devote many resources to quantum until production solutions are imminent. Then theyll spend a lot of money playing catch-up.

But every dollar they spend on forward-looking research gives their competitors a chance to beat them today.

A frustrating problem for tech innovators who need financial backing. One that applies across many sectors, though in areas such as cybersecurity and communications, where quantum-safe technologies are vital, and industries such as materials science and pharmaceuticals, early investment is a more immediate priority.

Yet being first out of the (quantum) gate is what drives Differential Ventures, a seed fund that backs promising early-stage companies in quantum, plus AI, machine learning, and data science. That said, seed-investment in the US can mean writing cheques for a few million dollars, as well as smaller, less risky sums.

Hotspots include bridging the gap between quantum technologies and classical environments, says Magerman. For example, his company recently led a $6.1 million seed extension round in Agnostiq, the Toronto-based distributed computing start-up that is building Covalent, an enterprise quantum and high-performance computing platform.

His company is also backing more personal artificial intelligence (AIs that query personally identifiable data, or PID), and their necessary flipside: private AIs, which keep PID safe from acquisitive AIs online. That certainly keeps the options open!

He explains the Differential philosophy:

A lot of people promote fantastical solutions that are oversold and aren't realistic. We'll avoid those companies. And those that are pitching things that aren't that hard, even if they're interesting applications. Once those ideas hit the mainstream, they are hard to protect from the competition, so we avoid those investments too.

Really, its based on my experience as a data scientist.

So, how common is demand for seed funding in quantum computing, an area often based in research labs and university spinouts, where high-risk, long-term, big-ticket VC backing would seem to be the order of the day? Massive funds that can afford to have a speculative under-performer in their portfolio when the other wins might be massive?

He says:

We typically avoid hardware companies, because they tend to require much more capital. And not only to start out: a quantum hardware company might raise a few million dollars in their first investment round, but they're going to require probably hundreds of millions to get to product.

But there are a lot of quantum software companies that typically want the seed rounds we look to invest in. They can get to product and get to revenue at scale, which means we can continue to invest in them before they get too big for us.

For example, Agnostiq recognized early on that the big problem impeding the growth of quantum research was orchestration tools that could integrate classical computing workloads with quantum computing resources. So, they built a product that allows companies to do high-performance computing orchestration.

So, does Magerman believe that a tipping point is approaching, or is it more a case of softly, softly? Because on the face of it, a $42 million seed fund (at present) could not afford to wait too long for commercial payback in such an uncertain space?

He explains:

I wouldn't say it's fast approaching, but I would say it's inevitable. And I think it will approach more quickly based on more intellectual and financial resources being devoted to solving the problem.

The subtext is the limits of Moores Law being reached, at which point quantum may offer a promising way forward: a new upward slope of increasing speed and power, assuming the challenge of noise in quantum circuits can be, if not solved, then at least made workable at scale.

He says:

The end of Moore's Law, plus the supply-chain issues that we had with silicon during COVID are among the factors contributing to the increased attention on quantum, accelerating the solutions to problems that prevent it from scaling.

For so long there has been this belief that, if you can continue to scale and get more power out of classical computing chips by engineering them with more densely packed circuitry, making them faster, and simply building more of them there is less pressure to develop alternative architectures and hardware.

But now we're seeing that we're becoming more limited in computing power [in the near future] yet we have more and more data, and more and more computing needs, especially with AI there'll be a lot more pressure to accelerate the development of quantum hardware.

There are still unsolved problems it is still too error prone and difficult to scale but these are practical problems, not theoretical ones. I wouldn't bet against the ingenuity of humans, especially when there's economic forces pushing for a solution!

So, the voices saying a tipping point wont happen were wrong?

He says:

I think the tipping point is more about coming up with the killer algorithm that is currently intractable. One that would take, effectively, infinite computing time in a classical environment, but which quantum computing could solve in a reasonable amount of time.

So, the two questions are: one, can you actually implement these algorithms in a quantum computing environment? Can you solve that problem? And, two, does the precise solution to the optimization perform substantially better, in practical problems, then the heuristic approximation?

Because it could be that the heuristic approximation is good enough and that getting the exact mathematical solution doesn't give you meaningfully better real-world performance.

So, the value of quantum will be finding the real-world problem where the heuristic solutions we are using today are inadequate and the quantum solution is substantially better.

So, what should enterprise decision-makers do in this push me, pull you environment of conflicting messages, not to mention the tactical pressure to innovate now, rather than place longer-term bets?

He says:

Theres a big education process that needs to happen, and that's going to need both a bottom-up and a top-down approach.

First, you have to get quantum researchers inside these corporate research groups, and give them the opportunity to experiment with solutions and promote those up the food chain to senior management.

And the top-down is getting management to make the hiring decisions, to bring in people who understand quantum technology well enough to do good research and prepare the company for quantum at scale.

Wise words from a brave investor who is prepared to be there in the early days of new ventures, even as the big guns of IBM, Google, Microsoft, et al, gear up in the background.

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Quantum commercialization softly, softly towards the inevitable future, says backer - Diginomica

04.26.23

***WATCH: Senator Murrays remarks and questioning***

Washington, D.C. Today, at a Senate Appropriations Commerce, Justice, Science, and Related Agencies subcommittee hearing on President Bidens FY24 budget request for the Department of Commerce, Senator Patty Murray (D-WA), Chair of the Senate Appropriations Committee, spoke about the need to build on investments that keep the U.S economy competitive, support innovation, and strengthen trade partnerships.

When we compete with our adversaries its not just a matter of who spends the most on defense, said Senator Murray. Its a matter of who has the strongest supply chains, who supports workers and growing businesses most, who makes sure our communities have the high-speed internet they need to do just about anything in the 21st Century, and who is on the cutting edge of technologyadvanced manufacturing, green energy, quantum computing, and more.

In particular, Senator Murray underscored just how critical it would be that the Biden administration have the resources necessary to effectively implement the bipartisan CHIPS and Science Act and how the investments of the Inflation Reduction Act would also help build a stronger clean energy economy and boost American manufacturing.

The Department is also using resources we provided in the CHIPS and Science Act to bring manufacturing jobs in things like quantum computing and chip manufacturing to states like minebecause weve got to make sure the jobs of the future are here in Americanot shipped overseas, stated Senator Murray. And the Inflation Reduction Act is actually ushering in a boom in green manufacturing for batteries, and electric vehicles, and more.

We know China is making investments in many of these same industries and trying to get the upper hand. So if we want to not just compete, but to lead the world, we cannot let our investments fall behind or fall victim to partisan pot shots, Senator Murray continued.

In her questioning, Senator Murray asked Secretary of Commerce Gina Raimondo how the National Oceanic and Atmospheric Administration (NOAA) plans to continue aiding salmon recovery in Washington state and protect important ecosystems in the Pacific Northwest from invasive species like European green crabs. Senator Murray and Congressman Derek Kilmer recently joined tribal leaders, environmental advocates, and experts to trap European green crabs and discuss the threat they pose to Washingtons states native species.

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