Kozminski Techblog

Quantum computing is an exciting new field that is rapidly gaining attention from scientists and researchers around the world. Unlike classical computers, which use binary digits (bits) to represent information, quantum computers use quantum bits, or qubits, which can be made from quantum mechanical systems with two states (I’ll explain that in a moment). For example, the spin of electrons can be measured up or down, or individual photons are polarized vertically or horizontally. This new type of computing promises to revolutionize the way we process information, making it possible to solve problems that are currently impossible for classical computers to solve.

The fundamental difference between classical computers and quantum computers is the way they store and manipulate information. In classical computers, information is stored in binary form, as either a 0 or a 1. In quantum ones, information is stored as quantum states, which are superpositions of 0s and 1s. This means that a quantum bit can be both a 0 and a 1 at the same time, making them much more powerful than classical computers. “Let’s look at example that shows how quantum computers can succeed where classical computers fail:

A supercomputer might be great at difficult tasks like sorting through a big database of protein sequences, but it will struggle to see the subtle patterns in that data that determine how those proteins behave.

Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.

A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. As the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.

Quantum algorithms take a new approach to these sorts of complex problems — creating multidimensional spaces where the patterns linking individual data points emerge. In the case of a protein folding problem, that pattern might be the combination of folds requiring the least energy to produce. That combination of folds is the solution to the problem. (IBM, “What is quantum computing?”)

Quantum computers have the potential to solve complex problems much faster than classical ones. For example, they can quickly find the prime factors of large numbers, which is a critical operation in cryptography. This makes quantum computing a potential game-changer in the field of cryptography, as they could break encryption codes that are currently considered unbreakable.

An interesting application of this computing type is in the field of machine learning. They can perform machine learning algorithms faster and more accurately than classical computers, which could lead to significant advancements in fields like speech recognition, natural language processing, and computer vision.

They can be used also to study complex chemical reactions, which could lead to the development of new medicines and materials. Simulating these reactions, can help researchers understand how these processes work and how they can be improved.

Despite the exciting potential, quantum computing is still a developing field, thus there are also some challenges that need to be addressed. One of the biggest challenges is maintaining the coherence of quantum states, which is necessary for quantum computers to function correctly. This is because these states are highly sensitive to their environment, external noise and even small perturbations can cause the states to collapse. Furthermore, their complexity makes them difficult to design, build and maintain. Currently, there are only a few them available and the hardware is expensive and difficult to access. Another challenge is the lack of scalability, meaning that they can only solve very specific problems and are not yet capable of general-purpose computing. The number of available quantum algorithms is also limited, and developing new algorithms is a challenging and ongoing process. Additionally, because quantum computers are very expensive to build and maintain, limiting their accessibility to only well-funded research institutions and large corporations.

What do we have to focus on in this area? The development of algorithms that can be run on quantum computers!

While there have been some advances in this area, there is still much work to be done to develop algorithms that can take full advantage of the power of quantum computing.

We can all agree that despite all challenges that this relatively new technology has to overcome, its future looks bright. With continued research and development, it is likely that we will see significant advancements in this field in the coming years. From cryptography to machine learning, it has the potential to revolutionize the way we process information and solve problems. It is definitely a very exciting field with the potential of changing the world as we know it. As research continues, we can expect to see more and more applications of it in fields like cryptography, machine learning, and chemistry.

Chat GPT – one of the hottest subjects of last days. It is already used by students, programmers and people across various fields. This artificial intelligence language model developed by OpenAI, was designed to provide human-like text-based conversations. It has gained significant popularity in recent years and is used in various applications such as customer service, language translation, and content creation. The growing interest raises important questions about the advantages and disadvantages of this technology, as well as the latest developments in the field. So, what can we say about them?

One of the most notable advantages of Chat GPT is its ability to provide human-like conversations. The model is trained on a vast amount of text data, which gives it the ability to understand and respond to a wide range of topics and questions. This makes it suitable for use in conversational interfaces, where a human-like response is desired. For example, it can be used to provide customer service through chatbots, or as a language translator for people speaking different languages.

What can we say about its versatility? The model can be trained to perform a wide range of language-related tasks, including question answering, summarization, and text generation. The mentioned versatility makes this AI model useful for a variety of applications, including content creation, language learning, and research. For example, it can be used to generate articles, create personalized learning experiences, or even help researchers study language patterns and behaviors.

Furthermore, it has the ability to continuously improve over time. As more data is fed into the model, it continues to learn and become even more human-like in its responses. This means that the model will become more accurate and capable over time, providing a valuable resource for those using it for various applications.

Moreover, a very exciting development in the field of Chat GPT is the use of the technology in the creative industries. For example, the model has been used to generate music, poetry, and even visual art. This opens up new possibilities for artists and creatives, and it will be interesting to see how the technology evolves and is used in the future.

While it has many advantages, there are also some significant disadvantages to consider. One of the most pressing concerns is the potential for bias in the model. Like any machine learning model, Chat GPT may be susceptible to bias, particularly in its training data. For example, if the training data contains gender or racial stereotypes, the model may reproduce these biases in its responses. This could lead to harmful consequences, such as perpetuating harmful stereotypes or making inaccurate predictions.

Many people are also cocerned about its impact on education. Are students going to be able to think on their own? Is this AI model going to influence their writing skills? Well, these are pretty good questions. Like many other things Chat GPT is only a tool which we can use to increase our efficiency, save time and reach out to in times of creative crisis (who doesn’t hate that?). Maybe we should learn how to use this tool in a way which increases our skills and is not harmful? “Ethan Mollick, an entrepreneurship and innovation professor at the University of Pennsylvania’s Wharton School, told NPR on Thursday that he now requires his students to use ChatGPT to help with their classwork. “This is a tool that’s useful,” Mollick said during the NPR interview. “There’s a lot of positives about it. That doesn’t minimize the fact that cheating and negativity are there, but those have been there for a long time.”  His new AI policy — which NPR reviewed — calls AI usage an “emerging skill.” The policy also states that students must check ChatGPT’s responses and will be held accountable for any inaccuracies that the bot spits out.” (businessinsider.com, Aaron Mok,”A Wharton business school professor is requiring his students to use ChatGPT”). Of course everything is only as useful as we make it. We cannot assume that something is 100% good or bad. It all depends on the way we use it.

What about the limitations? Although the model can provide human-like responses, it may still lack the context and emotional intelligence that a human would have in a conversation. For example, the model may struggle to understand sarcasm or humor, or to respond appropriately to emotionally charged situations. This lack of emotional intelligence can result in awkward or inappropriate responses, which may hinder the effectiveness of the model in certain applications.

Finally, there is a risk of the model being misused for malicious purposes. That’s right. It can be used to spread false information or impersonate people online, which could have serious consequences for individuals and society as a whole. This highlights the need for responsible use of Chat GPT, as well as for ongoing monitoring and regulation of the technology.

Despite these disadvantages, it is an exciting and rapidly developing field, with numerous recent developments worth mentioning. For example, OpenAI recently released a new version, called GPT-3, which has significantly improved language capabilities and has been integrated into various products and services. GPT-3 has received widespread attention, with many experts hailing it as a breakthrough in AI technology.

An exciting development in this field is also the recent release of a model that can generate realistic images. This model can be combined with Chat GPT to create photo-realistic fake news and deepfakes, which could have significant implications for politics, media, and society as a whole. This highlights the need for ongoing research and development in this area.

In the final analysis, it is a powerful and versatile AI model that has many potential applications. From customer service to content creation, the model provides businesses with new opportunities to engage with customers and process large amounts of text data. However, there are also several disadvantages to consider, such as bias and the potential for malicious use. As the technology continues to evolve and improve, it is important that businesses use it responsibly and in accordance with ethical guidelines. The hot news surrounding Chat GPT highlight the exciting potential of this technology and its impact on various industries.

First, let’s talk about cybersecurity. Cybersixgill in the report regarding their predictions of its’ future stated: “Cybercrime is increasingly lucrative,[…]we expect a record-breaking year of cyber security breach notifications not only because of the sophistication of thread actors but also due to larger changes in the world global unrest supply chain instability and soaring inflation will impact an organizations ability to mitigate remediate or prevent a problem.[…]AI will play a huge role both in cyber threat intelligence and in fighting cybercrime.”

Cybersecurity expert Dov Lerner said that “While the technology has been developing for a while , the difference now is that it’s more accessible. Advanced AI capabilities that were only in the hands of a few governments and researchers became mainstream in 2022 – think DALL-E, Stable Diffusion and chatGPT.  We think cybersecurity will go in this direction in 2023 as AI capabilities became available for both attackers and defenders.”

In terms of trends Mann predicts a lot of changes in analytics. It is a very dynamically developing and changing area. Its’ future holds a lot of exciting opportunities as well as threats. They mention that the 4 major trends in 2023 will be: “The rise of low-code and no-code automated machine learning (AutoML), enhanced digital twin technologies, industrial adoption of computer vision and a blurring of the lines between edge and cloud.”” Mann says we’ll also see more purpose-built digital-twin applications in 2023 specialized for defined use cases in energy, infrastructure optimization and industrial manufacturing sectors. Organizations are also expected to increasingly adopt CV and other AI technologies, with the kinds of industries harnessing these technologies expanding beyond more niche use cases by IT staff and data scientists. According to Mann, CV initiatives will focus on “yield improvement, operational efficiency and safety. Finally, with cloud hyperscalers like Microsoft Azure, Amazon Web Services and Google Cloud Platform starting to roll out core cloud services on the edge, edge computing will become an extension of cloud computing. Workloads will be distributed intelligently across hybrid environments. This will mean quicker adoption of IoT analytics at the edge in 2023 to enhance decision making at the source. […]These trends don’t mark a departure from previous years, but rather a continuation of market trajectories following the pandemic.”(“IoT World Today)

Some of the tech trends were presented by by Koenig is the CES convention (Consumer electronics show) organized by the Consumer Technology Association – one of the biggest tech events in the world. According to “IoT World Today” “With the potential of a 2023 recession, Koenig suggested that the market can expect to see four enterprise tech innovations: connected intelligence, autonomous systems, quantum computing and 5G industrial IoT applications.

The next generation of online experiences, the metaverse of things (MoT) and the technology innovations fueling it, were highlighted as digital twins, virtual spaces, shared experiences and virtual scenarios.

Koenig said highlights of transportation, one of the major themes of this CES, were the advancements of autonomous systems, the transformation of the in-vehicle experience and the evolution of the electrification ecosystem.

In-vehicle enhancements coming were noted as screenification, voice control, retail and entertainment services and features as a service (FaaS) models.

In health services, Koenig sees new frontiers in innovation, including anytime virtual visits, remote patient monitoring, fitness and wellness platforms and access to online pharmacies.

Koenig also spent some time detailing farming of the future, which is to include intelligent silos, drones and soil sensors, farming robots, connected farmers and satellite 5G connections.

One major farming innovation was the John Deere fully autonomous tractor we wrote about at the last CES. That tractor is the CES Innovation Award winner in robotics this year.”