Smart contracts, the self-executing agreements with the terms of the contract directly written into code, are revolutionising various industries by automating processes and reducing the need for intermediaries. These digital contracts, primarily built on the Ethereum blockchain, offer the promise of increased efficiency and transparency.

The integration of advanced automation tools into smart contracts is one of the most significant recent advancements. The Ava Protocols mainnet launch on Ethereum exemplifies this trend, enabling developers to incorporate enhanced transaction automation, privacy, and cost-efficiency into their decentralised applications (DApps).

This protocols ability to trigger autonomous super-transactions based on predefined conditions simplifies complex on-chain operations and reduces friction for both developers and end-users.

Moreover, the use cases for smart contracts continue to expand. Initially popular in finance for automating transactions, they are now being used in industries such as real estate, supply chain management, and even intellectual property. For instance, platforms like RealT and Propy facilitate fractional ownership of real estate, allowing investors to buy shares in properties without large capital outlays. Similarly, Maecenas and Masterworks have made it easier for investors to own shares in valuable artworks.

Experts in the field emphasise both the potential and the challenges associated with smart contracts. Chris Li, founder of Ava Protocol, highlights the efficiency and transparency brought about by automated smart contracts, which can streamline processes like dividend distributions and voting rights without manual intervention. However, he also points out the need for secure and resilient foundations to support these innovations.

From a technological perspective, smart contracts are highly dependent on the precision of their code and the security of the blockchain infrastructure. As Oded Vanunu, Chief Technologist at Check Point Software Technologies, notes, even minor flaws in smart contracts can lead to significant vulnerabilities, such as unauthorised access and fund misappropriation. To address these risks, it is essential to adopt a multi-faceted approach that includes formal verification tools, comprehensive auditing processes, and advanced encryption techniques.

Looking ahead, the expansion of tokenization into new asset classes and the evolution of regulatory frameworks are expected to shape the future of smart contracts. Tokenization can unlock value in assets like intellectual property and carbon credits, creating new investment opportunities.

Additionally, as regulators around the world begin to recognise the benefits of smart contracts, the development of clear and comprehensive regulatory frameworks will help reduce legal uncertainties and encourage greater adoption.

However, challenges remain. Scalability issues, security concerns, and the need for integration with traditional financial systems are key considerations for the future. The transition to Ethereum 2.0 aims to improve scalability and security, addressing some of these challenges. Ensuring seamless integration between smart contracts and existing financial infrastructure will also be crucial for their widespread adoption.

While smart contracts can revolutionize various industries, their success will depend on addressing technological, legal, and economic challenges. As advancements continue, the adoption of smart contracts is likely to grow, unlocking new opportunities for innovation and efficiency.

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Smart contract evolution and its technological impact - CoinJournal

A reader asks:

As someone that spends too much time thinking about buying a lake house, I would love to hear your thoughts. The argument that you will feel tied down and obligated to go to the lake house at the expense of other travel worries me.

A few weeks ago I wrote about my all-time high in savings and why it was a mistake. Heres something I shared:

But I no longer feel its necessary to go over and above when it comes to saving. I want to enjoy some of my money now while I can.

Thats the biggest reason our savings fell off a little in 2022 and 2023. We took a bunch of trips. We did some minor renovations to the house that added hangout spaces. We bought a boat. We own a lake house.

A few people asked if I would share my experience of owning a vacation house.

Were in a fortunate position to own a house on the lake but there was also some fortuitous timing involved along with some risk.

Our oldest daughter was 3 years old when our twins were born in 2017. We quickly realized travel was going to be nearly impossible what with all the car seats, strollers, pack-n-plays and such.

That same year our good friends were building a house on a lake so we got to spend some time in a new area. We loved it. And it just so happened that a brand new development was getting underway right on the water.

We had always discussed buying a lake house someday but never explored it too seriously until we saw this place. There were plans for a community pool, a marina, and a restaurant (that never happened), but the development was still in its infancy, so lots werent selling all that quickly. It was a risk, and prices reflected that.

We got a really good deal in 2018 and built a wonderful place right by the water. As they slowly but surely added more homes we decided we would eventually trade up when the second phase got underway and lots with better views opened up. Those conversations with the builder took place starting in 2019.

Then the pandemic hit. Plans to build more houses were shelved because of the supply chain issues. Our house skyrocketed in value. By the time we sold it in 2022, it was up ~75% in less than four years.

We used that equity to buy a new house in a better location with better lake views. The timing of that purchase will be the best investment well ever make.

The builder allowed us to lock in the price in late-2021, which was still reasonable at the time. The lender allowed us to lock in a rate before the construction even started when mortgage rates were still 3%. The house took 18 months to build as the construction industry slowly thawed. By the time it was done in the spring of 2023 mortgage rates were approaching 7% while the price of houses exactly like ours were going for 50% more than we paid.1

If you just base it on the down payments we made, were talking Nvidia-like returns here. Sure, we took some risk by buying one of the first homes in a new development but we got very lucky with how the timing of everything worked out.

Id call it 20% the risk we took2 and 80% luck.

Thats our story. Now lets discuss some pros and cons of owning a vacation home.

Lets start with the cons:

Two mortgages.Obviously, its more expensive.

Higher property taxes.The property taxes on a second home are much higher than a primary residence. The property taxes on our vacation house are roughly double what we pay on our primary home.

The upkeep. Taking care of another property when you dont live there full-time an be challenging.

Luckily, were part of an association that handles landscaping, snow removal and upkeep. Thats another monthly cost (even though its worth it).

Its also a pain to take deliveries and get things serviced since youre not there all the time.

Usage. I saw a statistic estimating that people spend an average of about six weeks a year in their vacation homes.

Were higher than that (it helps that our place is only an hour away from home base) butsome people might not use it enough to justify the cost.

I love going to the lake most weekends but some people might prefer more variety.

Concentration risk.Owning multiple homes makes you concentrated in the housing market. We have a big chunk of our net worth invested in Michigan real estate.

Im OK with this risk but its a risk nonetheless.

Now for the benefits:

It feels like a vacation every time. The anticipation of a vacation often gives you the biggest psychological boost. I get that feeling on a weekly basis in the summer.

I still get excited every time we head north.

More time spent outdoors.We spend far less time indoors looking at our phones, social media or television.

Our kids are outside all the time swimming, boating, fishing, going for walks, playing on the beach, going for bike rides, etc.

We could do more of this at home, but having a lake house forces you to be outside more.

You could rent it out.Our association doesnt allow Airbnbs and the like but I know some people who own a second home who rent it out to help cover the costs.

Remote semi-retirement. My job affords me the flexibility to work from anywhere.

We often extend our weekends in the summer to spend Friday-Monday at the lake. We were there for ten days around the 4th of July.

The ability to combine work with relaxation and fun is one of my favorite parts of having another place.

The water.Being around water puts me in a better mood.

[my happy place]

Its also a valuable selling point as an investment. A friend told me a house on the water will never go down in value.

This is an exaggeration but probably right 90% of the time.

The memories. Buying a second home was the best/luckiest financial decision weve ever made.

But even if we lost money on our second house experiment, the investment would have been worth it.

Were creating memories that will last a lifetime and its hard to put a price on that.

The best thing about having a vacation home is that it provides an excuse to spend more time with friends and family.

I discussed this question on Ask the Compound:

Bill Sweet joined me on the show to discuss questions about executing a die with zero retirement strategy, changing tax status on your tax return, tax-optimizing your retirement distributions and muni bond funds.

Further Reading: My All-Time High in Savings

1Prices caught up in a hurry. The first-mover advantage helped here too.

2We had a few friends, family members and unnamed financial advisors try to talk us out of it. Im glad they didnt.

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The Best Investment I Ever Made (the Pros & Cons of Owning a Vacation Home) - A Wealth of Common Sense

The Cryptocurrency Market: Mixed Signals and Emerging Opportunities

The cryptocurrency market has been experiencing mixed signals in recent times. Similarly, Binance Coin (BNB) and Polkadot (DOT) are witnessing price declines potentially triggered by broader political uncertainties. Conversely, analysts are voicing optimism for Clandeno (CLD), a project currently conducting its Initial Coin Offering (ICO) which is garnering investors attention because of its promising decentralized e-commerce offering. Lets find out more.

Throughout this bull run, the Binance Coin (BNB) price has seen significant swings as buyers and sellers compete for dominance. For nearly the whole of last month, Binance Coin (BNB) fluctuated between $460 and $635. Moreover, the Binance Coin (BNB) price chart reveals that the coin dropped by over 10% after retesting the $600 zone after attaining an ATH.

Given that the bears were able to breach the $560 and $550 levels, Binance Coin (BNB) successfully dropped below $500 before beginning to reverse. If the market sustains a rebound, it could indicate strong demand, even at reduced prices. But to indicate that the correction is complete, bulls will need to push Binance Coin (BNB) above the $560 and $600 thresholds. Meanwhile, Binance Coin (BNB) investors are looking for reliable alternatives to diversify and Clandeno (CLD) makes the cut.

Funding for a game development project inside the Polkadot (DOT) ecosystem, Dot Play, has come from the Decentralized Futures Grant. Sponsored by the Web3 Foundation, this major project seeks to revolutionize the gaming sector using Polkadots (DOT) natural technological development to produce a complete gaming platform for game developers on Polkadot.

Dot Play will concentrate on the hottest game types and provide the required tools needed for developers to include their games on the Blockchain, with the main goal of building a gaming hub inside the Polkadot (DOT) ecosystem. They will also provide sustainable business development help so these games flourish over time. Amid this development, the revolutionary concept of Clandeno (CLD) in the e-commerce sector is also drawing in Pokadots investors.

Clandeno (CLD) is revolutionizing e-commerce with a creative distributed system that lets users buy and sell safely and transparently, thus eliminating the need for middlemen. Imagine a market where consumers have total control over their transactions free from outrageous fees and limited rules. Clandeno (CLD) is rendering this vision to life.

Meanwhile, both Binance Coin (BNB) and Polkadot (DOT) investors find the Clandeno (CLD) project promising, hence making moves to secure their slots in the presale. Moreover, being a presale participant will provide you with special advantages as well as the chance to help create a more democratic and inclusive global economy. Now is a perfect time to guarantee your place in the Clandeno (CLD) presale.

To find out more about the Clandeno presale, visit their website here

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Binance Coin (BNB) and Polkadot (DOT) Drop as Political Uncertainty Grows, While Analysts Tip Clandeno (CLD) for Explosive Growth; Initial Coin...

In a significant leap for the field of quantum computing, Google has reportedly engineered a quantum computer that can execute calculations in mere moments that would take the worlds most advanced supercomputers nearly half a century to process.

The news, reported by the Daily Telegraph, could signify a landmark moment in the evolution of this emerging technology.

Quantum computing, a science that takes advantage of the oddities of quantum physics, remains a fast-moving and somewhat contentious field.

Quantum computers hold immense promise for potentially revolutionizing sectors like climate science and drug discovery. They offer computation speeds far beyond those of their classical counterparts.

However, this advanced technology is not without its potential drawbacks. Quantum computers pose significant challenges for contemporary encryption systems, thus placing them high on the list of national security concerns.

The contentious discussion continues. Critics argue that, despite the impressive milestones, these quantum machines still need to demonstrate more practicality outside of academic research.

Googles latest iteration of its quantum machine, the Sycamore quantum processor, currently holds 70 qubits. This is a substantial leap from the 53 qubits of its earlier version. This makes the new processor approximately 241 million times more robust than the previous model.

As each qubit can exist in a state of zero, one, or both simultaneously, the capability of storing and processing this level of quantum information is an achievement that even the fastest classical computer, however rapid or slow, cannot match.

The Google team, in a paper published on the arXiv pre-print server, remarked, Quantum computers hold the promise of executing tasks beyond the capability of classical computers. We estimate the computational cost against improved classical methods and demonstrate that our experiment is beyond the capabilities of existing classical supercomputers.

Even the currently fastest classical computers, such as the Frontier supercomputer based in Tennessee, cannot rival the potential of quantum computers.

These traditional machines operate on the language of binary code, confined to a dual-state reality of zeroes and ones. The quantum paradigm, however, transcends this limitation.

It remains uncertain how much Googles quantum computer cost to create. Regardless, this development certainly holds the promise of transformative computational power.

For instance, according to the Google team, it would take the Frontier supercomputer merely 6.18 seconds to match a calculation from Googles 53-qubit computer.

However, the same machine would take an astonishing 47.2 years to match a computation executed by Googles latest 70-qubit device.

Many experts in the field have praised Googles significant strides. Steve Brierley, chief executive of Cambridge-based quantum company Riverlane, labeled Googles advancement as a major milestone.

He also added: The squabbling about whether we had reached, or indeed could reach, quantum supremacy is now resolved.

Similarly, Professor Winfried Hensinger, director of the Sussex Centre for Quantum Technologies, commended Google for resolving a specific academic problem tough to compute on a conventional computer.

Their most recent demonstration is yet another powerful demonstration that quantum computers are developing at a steady pace, said Professor Hensinger.

He stressed that the upcoming critical step would be the creation of quantum computers capable of correcting their inherent operational errors.

While IBM has not yet commented on Googles recent work, it is clear that this progress in the realm of quantum computing has caught the attention of researchers and companies worldwide.

This will open new prospects and competition in the evolution of computational technology. Let the games begin!

Quantum computing, a remarkable leap in technological advancement, holds the potential to redefine our computational capacities.

Harnessing the strange yet fascinating laws of quantum physics, it could significantly outperform classical computers in solving certain types of problems.

Traditional computers operate based on bits, which can be in a state of either 0 or 1. Quantum computers, on the other hand, operate on quantum bits, known as qubits. Unlike traditional bits, a qubit can exist in both states simultaneously, thanks to a quantum principle called superposition.

Superposition increases the computing power of a quantum computer exponentially. For example, two qubits can exist in four states simultaneously (00, 01, 10, 11), three qubits in eight states, and so on. This allows quantum computers to process a massive number of possibilities at once.

Another key quantum principle quantum computers exploit is entanglement. Entangled qubits are deeply linked. Change the state of one qubit, and the state of its entangled partner will change instantaneously, no matter the distance. This feature allows quantum computers to process complex computations more efficiently.

The unusual characteristics of quantum computing make it ideal for solving complex problems that classical computers struggle with.

Cryptography is a notable area where quantum computing can make a significant difference. The capacity to factor large numbers quickly makes quantum computers a threat to current encryption systems but also opens the door for the development of more secure quantum encryption methods.

In the field of medicine, quantum computing could enable the modeling of complex molecular structures, speeding up drug discovery. Quantum simulations could offer insights into new materials and processes that might take years to discover through experimentation.

Despite its promising potential, quantum computing is not without challenges. Quantum states are delicate, and maintaining them for a practical length of time known as quantum coherence is a significant hurdle.

The slightest environmental interference can cause qubits to lose their state, a phenomenon known as decoherence.

Quantum error correction is another daunting challenge. Due to the fragility of qubits, errors are more likely to occur in quantum computations than classical ones.

Developing efficient error correction methods that dont require a prohibitive number of qubits remains a central focus in quantum computing research.

While quantum computing is still in its infancy, the rapid pace of innovation signals a promising future. Tech giants like IBM, Google, and Microsoft, as well as numerous startups, are making significant strides in quantum computing research.

In the coming years, we can expect quantum computers to continue growing in power and reliability. Quantum supremacy a point where quantum computers surpass classical computers in computational capabilities may be closer than we think.

Quantum computing represents a thrilling frontier, promising to reshape how we tackle complex problems. As research and development persist, we inch closer to unlocking the full potential of this revolutionary technology.

Supercomputers are high-performance computing machines capable of processing data at super high speeds in comparison to conventional computers.

Renowned for their significant computational power, they perform tasks involving complex calculations that typical computers cannot manage.

Scientists, researchers, and governments use supercomputers to solve intricate problems in areas like quantum physics, weather forecasting, climate research, and biochemical modeling.

The history of supercomputers dates back to the 1960s when the first supercomputer, CDC 6600, designed by Seymour Cray at Control Data Corporation, made its appearance.

Over the years, supercomputers underwent numerous advancements, transitioning from single processor systems to parallel computing designs.

The advent of parallel computing in the 1970s and 1980s allowed supercomputers to increase their computing power exponentially. This involved the use of more than one processor to divide tasks and conduct computations simultaneously.

In the 1990s, massively parallel computers like the Thinking Machines CM-5 started utilizing thousands of processors, marking a significant leap in supercomputing power.

Supercomputers possess unique designs and architectures to accommodate their advanced computing needs. Initially, vector processors were common in supercomputers, but with technological advancements, scalar processors and parallel processing became more prevalent.

Contemporary supercomputers use a variety of architectures. The majority utilize a massively parallel processing (MPP) approach. MPP allows supercomputers to divide large tasks into smaller ones for simultaneous processing by multiple processors.

Some supercomputers also use grid computing where they link geographically dispersed computers to form a supercomputer.

The architecture of a supercomputer requires meticulous planning and design to accommodate the heat generated by the processors and ensure efficient data transmission. As such, engineers design the infrastructure and cooling systems in a way that maximizes performance and minimizes energy usage.

The performance of supercomputers is typically measured in FLOPS (Floating Point Operations Per Second), a unit that indicates the speed of calculations. The fastest supercomputers today perform at exaFLOPS levels, that is, they can perform a quintillion floating-point calculations per second.

To rank supercomputers based on their performance, the Top500 project publishes a list twice a year. The rankings depend on a supercomputers performance in running the LINPACK benchmark, a software library that measures a machines ability to solve dense systems of linear equations.

Supercomputers find applications in diverse fields. In weather forecasting, they simulate climate models to predict future weather conditions.

The field of space exploration uses supercomputers to simulate and model celestial bodies and galaxies. In the field of physics, supercomputers perform complex simulations like particle collision in particle physics and nuclear fusion experiments.

Moreover, supercomputers play a pivotal role in medical research, helping to model and understand the structures of viruses, bacteria, and other microscopic organisms.

They also facilitate drug discovery and development by simulating the interaction of molecules with biological targets. Governments also use supercomputers for cryptanalysis, decoding encrypted data for national security purposes.

Supercomputers have played, and continue to play, a critical role in scientific discovery and technological advancement. By pushing the boundaries of computational power, they enable the resolution of complex problems across a multitude of domains, ranging from meteorology to quantum physics.

As technologies like quantum computing evolve, the potential of supercomputers will continue to expand, revolutionizing the landscape of high-performance computing.

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Quantum computer built by Google shatters calculation records - Earth.com

At the Tech.eu Summit in London, Dr. Ken Urquhart, Global Vice-President of 5G/Edge/Satellite at Zscaler, and Steve Brierley, Founder and CEO of Riverlane, discussed the critical intersection of artificial intelligence (AI), cybersecurity and quantum computing. Moderated by Duygu Oktem Clark, Managing Partner at DO Venture Partners, the talk underlined both the challenges and opportunities these technologies present.

Urquhart opened the discussion by addressing the limitations of AI in cybersecurity.

AI, as we apply it today, involves algorithms that are interpretable and useful for cyber defense, he said. However, he pointed out that current AI technologies, such as neural networks and large language models, come with issues like statistical variability and hallucinations, where the AI makes things up that may not be true.

Urquhart explained that these statistical models could become less accurate over time, adding: You need to be thoughtful about how you apply AI because it can give less accurate answers if asked the same question twice in a row over a span of hours or days.

Brierley shared his thoughts into the advancements in quantum computing and its implications for cybersecurity. He noted that while todays quantum computers are extremely error-prone and capable of only about 100 to 1,000 operations before failure, significant progress is being made with quantum error correction.

Quantum error correction is a layer that sits on top of the physical qubits and corrects errors in real-time, Brierley explained.

This development is crucial for achieving cryptographically relevant quantum computing capabilities.

2023 and 2024 have been pivotal years as we crossed the threshold in various qubit modalities, making error correction viable, he said. Brierley projected that within the next two to three years, we could see quantum computers performing up to a million operations, surpassing what classical computers can simulate.

As AI and quantum computing advance, ethical and security challenges emerge. Urquhart stressed the importance of understanding AIs current limitations.

We are on a journey with artificial intelligence. It does not think; it is a collection of statistical outcomes, he stated. Urquhart warned against over-reliance on AI for critical decisions, as its current form can lead to significant errors.

Brierley added that quantum computing has the potential to revolutionize industries, particularly in simulating molecular dynamics and chemical interactions.

Quantum computers can replace time-consuming lab experiments with simulations, transforming industries like drug discovery and material science, he said.

Both experts agreed on the necessity of collaboration among academia, industry and government to harness these technologies responsibly. Brierley called attention to the importance of a coordinated effort, likening it to a Manhattan-scale project to build the worlds most powerful quantum computers. We need effective collaboration across sectors to ensure the technology benefits society, he said.

Urquhart echoed this sentiment, giving emphasis to the role of commercial entities in driving innovation and the governments role in providing a regulatory and funding environment.

The machinery is there; we just need the will to engage and make it run, he remarked.

Looking ahead, both Urquhart and Brierley stressed the urgency of preparing for the impact of quantum computing on cybersecurity.

Quantum computing will break most encryption at some point, Urquhart warned, urging businesses to act now to mitigate future risks.

Brierley concluded: Quantum computers are not just faster computers; they represent a massive step forward for specific problems, and their potential for both good and bad is immense.

The discussion underscored the transformative potential of AI and quantum computing while cautioning against complacency. As these technologies evolve, proactive collaboration and ethical considerations will be paramount in shaping a secure digital future.

Featured image: Credit: Tech.eu

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The Interplay of AI, Cybersecurity & Quantum Computing - The Quantum Insider

IBM Quantum System Two modular quantum computing platform

Over the past year, there has been increasing focus on how quantum computers fit into and link to classic computing architectures. Quantum computers could act as an accelerator to perform complex calculations for certain tasks that are beyond the capabilities of even classical supercomputers. The classical computers or servers are used for preprocessing in the development of quantum algorithms and circuits and for postprocessing to manage the errors, improve the results, and complete the processing task. As is evident from the growing number of AI use cases, AI can enhance classical computing capabilities. So, it stands to reason that AI could also enhance quantum computing capabilities and several companies are working towards achieving this goal.

Even though many people and companies are starting to combine quantum and AI into a single term, the two are very distinct technologies. AI is the training and use of neural network models developed and run on classical computing platforms powered by CPUs, GPUs, NPUs, DSPs, FPGAs, and other traditional binary-processing logic elements. Quantum computers use alternative compute architectures, such as superconducting transmon qubits, to solve very complex problems using quantum physics. While the two require different hardware, software, and support systems, the integration of the two is moving forward, especially for the benefit of quantum computing. IBM is one of the companies paving the way for AI to complement quantum computing development.

IBM is considered the leader in the quantum computing segment with continued advancements in hardware, software, and systems technologies, and with development quantum computers already deployed around the world. IBM is also a leader in AI technology through its watsonx platform, which has logged many advances beginning with its Jeopardy game show win in 2011. Since then, watsonx has evolved to a scalable enterprise platform with the AI studio, data, governance, and assistant solutions. Now IBM is bringing the two technologies together to enhance quantum computing and accelerate its adoption.

In a recent discussion with IBM, the company outlined how it is integrating its AI technology into the Qiskit software to improve the ease of use of the SDK tools and OpenQASM3 (open quantum assembly language). IBM is using its watsonx generative AI platform, leveraging the companys Granite AI model, to generate digital agents capable of providing developer support and quantum code assistance.

In addition, IBM is researching and developing new AI models to improve other critical aspects such as circuit optimization, resource management, and improved error suppression, mitigation, and correction.

As part of its commitment to integrating AI into quantum computing, IBM is also introducing the Qiskit Code Assistant service with a Visual Studio Extension and plans to offer two quantum chatbots one to assist developers and the other to general users of IBM Quantum services.

In terms of circuit optimization, AI models can be embedded as plugins to the Qiskit SDK through a transpiler service or be combined with heuristic methods. According to IBM, the transpiler service provides better mapping of abstract circuits to quantum ISA circuits resulting in up to a 40% improvement in circuit size, better quality, and a 2x to 5x improvement in processing speed.

For resource management, IBM is developing AI solutions to better estimate the quantum runtime, flag workloads that are likely to fail, and partition circuits for parallel processing to better utilize both the classical and quantum resources. This includes leveraging AI supercomputers.

Future heterogeneous data centers will include QPUs

Combined with IBMs aggressive roadmap to reach 100 million gates by the end of the decade and 1 billion gates around 2033, quantum computing is rapidly moving toward the deployment of practical quantum applications over the next few years. As a result, we may begin to see heterogeneous data centers that combine the performance of the latest CPUs, AI accelerators, and QPUs (quantum processing units) by the end of the decade.

IBM Quantum Development & Innovation Roadmaps

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IBM Develops The AI-Quantum Link - Forbes

Quantum computing is now at its early stage, and its evolution is a new epoch that will bring about changes across the domains of life. Quantum computers, however, do not operate like classical computers, which use binary cycles of 0 and 1, known as bits. Still, they use the principles of quantum mechanics to solve very complex computations.

Quantum computers have quantum bits or qubits. In quantum computing, quantum bits can be in multiple states at the same time through superposition. These principles allow quantum computers to solve problems beyond the capacity of other compact computing systems today.

Given below are some ways, in which quantum computers are revolutionizing scientific research:

Just like quantum chemistry is expected to revolutionize chemical science by simulating the interaction between molecules and atoms, quantum computing is expected to do the same. Quantum Computers will help advance the search for materials optimized for certain functions or characteristics, like superconductors, though with a B2B use case.

The possibility of perfectly imitating the specimens of chemical reactions can lead to groundbreaking discoveries across numerous industries, such as the medical field and farming.

By utilizing quantum bits, quantum computers can simulate the interactions at the quantum state and get results that cannot be acquired using conventional computers. They can also elicit novel drugs and more effective catalytic agents for some chemical reactions.

Quantum computing holds the potential to revolutionize healthcare and medicine in several profound ways:

In traditional drug discovery, it takes considerable time and money to develop a drug that may take years. Considering issues related to the simulation of chemical compounds and molecular structures, it is possible to solve problems concerning the creation of new drugs millions of times faster and more efficiently than traditional computers.

It is through quantum computing, that can relate large amounts of genetic data to facilitate accurate medical treatments. These could include advancements in the investigation of viral and genetic disorders and the development of new gene therapies for various types of genetic mutations.

Despite its distinct advantages, quantum computing poses serious threats to modern approaches to cybersecurity. Cryptographic algorithms that are currently applied on a large scale can be easily opposed by quantum computers because factoring large numbers is only difficult for todays classical computers. At the same time, quantum algorithms can solve the same task exponentially faster.

In a bid to counter these risks, scientists are developing quantum-resistant encryption techniques. These new cryptographic techniques will help ward off those who attempt to breach the security measures in place by using state-of-the-art quantum machines, safeguarding information from being compromised in a post-quantum era.

QKD works on the foundation of Quantum mechanics by designing secure links. If an effort is made to run a wire and listen into the communication, the quantum state is changed, thus notifying the owners and making sure the information exchanged is safe. It may not be far off statement to describe this technology as potentially bringing secure communication as close to becoming virtually inviolable as would be possible.

Quantum computing will vastly improve data processing capabilities, offering solutions to problems currently beyond the reach of classical computers:

Every industry has challenging tasks and objectives to solve, varying from supply chain management to financial analysis and logistic scenarios. By using quantum computers, large amounts of data can be processed, and the best solutions to problems can be achieved rapidly as compared to classical methods, ultimately reducing cost.

Quantum computing will further improve AI through improved machine learning algorithms and, hence, better data analysis. This will culminate in the creation of better artificial intelligence systems for addressing complex issues and, as such, making better forecasts.

The financial sector stands to benefit enormously from quantum computing:

One of the significant benefits of quantum computing is pattern matching or pattern recognition, as it works on large datasets, while most classical computing might fail to do so. Therefore, if applied in modeling risk assessment, it will help financial institutions manage risks better through better decision-making.

Quantum algorithms can completely process and analyze market data, identify trading opportunities, and manage portfolios most efficiently. This could result in higher returns, which is the ultimate goal of investing, and more stability in financial markets.

Quantum computing will also play a pivotal role in advancing space exploration:

It is critical to note that managing a space missions efficiency means solving challenging multi-objective optimization issues. Another application is that quantum computers can also, for instance, design trajectories for actual spacecraft with less consumption of fuel or other resources, thus making the costs of the mission less.

Digital imitation of the behavior of astronomical objects and phenomena is a difficult process that demands a great deal of computational resources. Quantum computers are capable of providing more precise simulations than classical computers, so the world gains a clearer vision of the universe, and discoveries can be made.

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How Quantum Will Change Everything in Forthcoming Years - Analytics Insight

Jun 24 2024Reviewed by Lexie Corner

In a studypublished in PeerJ Computer Science, Professor Kazuhiro Ogataand Assistant Professor Canh Minh Do of the Japan Advanced Institute of Science and Technology (JAIST) suggested using symbolic model checking to validate quantum circuits.

Quantum computing is a fast-developing technology that utilizes the principles of quantum physics to tackle complicated computational problems that are extremely difficult for classical computing.

To take advantage of quantum computing, researchers worldwide have created a large number of quantum algorithms that show notable gains over classical algorithms.

Creating these algorithms requires the use of quantum circuits, which are models of quantum processing. Before they are actually deployed on quantum hardware, they are utilized to design and implement quantum algorithms.

Quantum circuits consist of a series of quantum gates, measurements, and qubit initializations, among other events. Quantum gates execute quantum computations by working on qubits, the quantum equivalents of conventional bits (0s and 1s), and manipulating the system's quantum states.

Quantum states are the output of quantum circuits that can be monitored to provide classical outcomes with probabilities from which additional actions can be taken. Since quantum computing is frequently counterintuitive and substantially distinct from classical computing, the likelihood of mistakes is significantly larger. As a result, it is critical to ensure that quantum circuits have the correct features and perform as planned.

This can be accomplished using model checking, a formal verification approach used to ensure that systems meet desirable attributes. Although certain model checkers are specialized to quantum programs, there is a distinction between model-checking quantum programs and quantum circuits due to differences in representation and the absence of iterations in quantum circuits.

Considering the success of model-checking methods for verification of classical circuits, model-checking of quantum circuits is a promising approach. We developed a symbolic approach for model checking of quantum circuits using laws of quantum mechanics and basic matrix operations using the Maude programming language.

Canh Minh Do, Assistant Professor, Japan Advanced Institute of Science and Technology

Maude is a high-level specification/programming language based on rewriting logic that enables the formal definition and verification of complicated systems. It comes with a Linear Temporal Logic (LTL) model checker that determines if systems meet the necessary features. Maude also enables the development of exact mathematical models of systems.

Using the Dirac notation and the rules of quantum physics, the researchers formally defined quantum circuits in Maude as a set of quantum gates and measurement applications. They provided the systems intended attributes and its initial state in LTL.

By using a set of quantum physics laws and basic matrix operations formalized in our specifications, quantum computation can be reasoned in Maude.The researchers then automatically checked whether quantum circuits satisfied the required characteristics using the integrated Maude LTL model checker.

Using this method, several early quantum communication protocols, each with increasing complexity, were checked: Superdense Coding, Quantum Teleportation, Quantum Secret Sharing, Entanglement Swapping, Quantum Gate Teleportation, Two Mirror-image Teleportation, and Quantum Network Coding.

They discovered that the initial iteration did not meet the desired property of Quantum Gate Teleportation. By employing this method, the researchers suggested an updated version and verified that it was accurate.

These findings highlight the significance of the suggested novel technique for the verification of quantum circuits. However, the researchers highlight certain drawbacks of their strategy that need more investigation.

Dr. Do added, In the future, we aim to extend our symbolic reasoning to handle more quantum gates and more complicated reasoning on complex number operations. We also would like to apply our symbolic approach to model-checking quantum programs and quantum cryptography protocols.

Verifying the expected functionality of quantum circuits will be extremely useful in the approaching era of quantum computing. In this context, the current technique is the first step toward a broader framework for verifying and specifying quantum circuits, opening the way for error-free quantum computing.

The study was supported by JST SICORP Grant Number JPMJSC20C2, Japan, and JSPS KAKENHI Grant Numbers JP23H03370, JP23K19959, and JP24K20757.

Do, C. M., etal. (2024) Symbolic model checking quantum circuits in Maude. PeerJ Computer Science. doi:10.7717/peerj-cs.2098

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The Road to Error-Free Quantum Computing - AZoQuantum

International Day of Yoga is celebrated worldwide on June 21. In 2015, when this was first commemorated, some 36,000 enthusiasts including the Indian prime minister, Narendra Modi, and dignitaries from 84 countries lined up in New Delhi, for the worlds largest yoga session.

Sessions were also held on the bank of the River Thames in London and under the Eiffel Tower in Paris. Last year, Modi led a yoga session at the UN headquarters in New York. Also last year, the Indian city of Surat hosted the worlds largest yoga session, with over 150,000 participants.

Yoga has thoroughly permeated global consumer culture and everyday news ranges from the glamorous to the absurd. The Louvre has announced yoga classes for visitors to coincide with the Olympics in Paris in June. Italy recently banned puppy yoga, saying only adult dogs could participate, for reasons of animal welfare.

UNIVERSAL APPEAL

According to one estimate, some 300 million people practise yoga worldwide. Yoga has also made significant inroads in Pakistan over the last decade. A quick search in major cities reveals an abundance of programmes and studios.

The ancient practice of yoga is a worldwide phenomenon due to its many touted mental and physical health benefits. But do these claims stand up to scientific scrutiny?

Many of these options are backed up with glossy Instagram feeds of models, twisting their bodies into the trademark poses, once so fantastical but now entirely familiar. In May, Islamabads Capital Development Authority launched complimentary classes for residents in the F-9 Park.

But beyond the glamour and buzz, the average person has questions: what is all this hype about? How is yoga different

from other fitness routines? Is it actually something special?

FROM THE SCIENTIFIC LENS

Some of yogas scientifically documented benefits are only to be expected: studies show that yoga significantly improves flexibility; it helps combat arthritis; yoga is effective against carpal tunnel syndrome; it alleviates chronic lower back pain; and it seems to be a promising aid in weight loss.

Yoga has a pronounced meditation component, and studies show significant stress reduction, which can have cascading effects on reducing risk of heart attacks, strokes, chronic disease, etc.

But when one starts to delve deeper, things get interesting fast.

Consider an early and intriguing study from Duke University, which compared health benefits of yoga with aerobics for almost a hundred adults.

At the time of the study, in 1989, the magical secret to fitness was to increase ones maximal oxygen uptake, the VO2 max, ie the maximum volume of oxygen consumed during rigorous exercise. Decades of science and research demonstrated that aerobic exercise did precisely that, making it the dominant fitness paradigm of the era. The study results were also very clear: over the four-month study period, subjects participating in aerobics raised their VO2 max significantly. There was no increase for the yogis at all.

However, when researchers surveyed participants quality of life, the response was overwhelmingly positive for both groups. At the end of the study, yogis also felt healthier, they reported higher energy levels, endurance, flexibility and better sleep. Their social lives improved markedly. Memory and concentration were enhanced. They had less loneliness, improved self-confidence and life satisfaction. They even felt that they looked better.

THE FOUNTAIN OF YOUTH?

The literature on yoga abounds with counterintuitive findings like these. Another fascinating study from 2008 confronts the popular claim that yoga is restorative and anti-ageing. Could yoga really play a role in human longevity?

Biological ageing is linked to telomeres, which are DNA strands at the very tips of chromosomes. Every time a cell divides, these tips get shorter, making them a kind of clock, indicating the cells lifespan. This discovery an alternative way of counting biological age was significant enough to secure the Nobel Prize.

In their study, researchers investigated a group of 30 men with low-risk cancer and introduced comprehensive lifestyle changes for them, including a low-fat diet, a walking routine, and yoga-based stretching, breathing and meditation.

After three months, they reported substantial health improvements, including decreased blood pressure, cholesterol, body mass index, etc. The researchers also discovered that telomerase activity the enzyme that counteracts the shortening of telomeres increased by 30 percent.

A follow-up study five years later, featuring 10 of the same subjects, confirmed significant increases in telomere length.

The authors noted the potential of these findings for cellular longevity, tissue renewal, disease prevention, and increases in life span. Even in the understated language of science, this is bold new fountain-of-youth territory.

AN AURA OF SPIRITUALITY

Yoga has always had this mystique, a touch of the exotic and the supernatural.

In 1965, B.K.S. Iyengar the man who did more than any other to popularise yoga in the West wrote in his seminal book, Light on Yoga, how this practice can bring one to the crossroads of his destiny.

I remember coming across an old book, in a library overseas, with the intriguing title Christian Yoga. Written in the sixties by a French priest, J.M. Dechanet, the book was an intimate and inspiring memoir of his experiment to reconcile yoga with the Christian tradition.

Early in the book, he notes that reading the Bible made the contemplative lives of prophets seem distant from our noisy modern existence. Later, he realises that practising yoga, surprisingly, allowed him to experience, to an extent, the serene calm hed read about.

These are very interesting claims. We see a faint reflection of these in the science. For instance, a review paper surveying some 30 studies finds that [yoga] may be positively associated with several aspects of spirituality.

In his book, A Life Worth Breathing, author Max Strom describes a complaint that many of us will find familiar: In the morning I cant wake up, in the day I am bored, in the evening I am tired, and at night I cant sleep.

Yoga can be a wonderful antidote. Even a few weeks of practice are enough to realise that yoga facilitates a contemplative state.

Multiple surveys from different countries find that, whereas most people start yoga for its physical benefits, a large number end up maintaining the practice, primarily for its spiritual side-effects.

MORE STRENOUS THAN SPORTS

However, for those who may be motivated to jump on to a mat right away, it is important to sound a note of caution. The good news is that statistics on yoga injuries are largely reassuring: the rate is low.

A Danish study of almost 3,500 participants reported an injury prevalence of one percent for yoga. To get a sense of comparison, this figure was 38 percent for soccer players, 19 percent for runners and nine percent for those undertaking strength training. However, the bad news is that there are abundant accounts of injuries and harms.

Journalist William Broad, author of the highly recommended book The Science of Yoga, comments that [yoga] makes most other sports and exercises seem like childs play.

There are reports of students pushing their bodies beyond their limits to achieve challenging poses, resulting in torn tendons, popped ribs and blood clots. When one digs into these incidents, two main reasons pop out.

One is basic common sense: a wide-ranging survey of yoga teachers, therapists and clinicians finds that the most commonly cited culprits were [p]oor technique or alignment, previous injury, excess effort, and improper or inadequate instruction.

The second reason behind injuries is more serious and more subtle: ego. Some people tend to bring a materialist and competitive drive to yoga and rush themselves into advanced poses, out of a sense of achievement. But the pose should always be part of the journey and not the goal. It is vital to listen to the body attentively.

To quote author Max Strom again regarding yogas transformative magic: Remember, it doesnt matter how deep into a posture you go what does matter is who you are when you get there.

Like most quotes on yoga, this, too, can be maddeningly cryptic for an outsider. At the end of the day, the secret of yoga cannot really be explained. Like many of the truly good things in life, it can only be experienced.

The writer teaches at the NUST School of Electrical Engineering and Computer Science, Islamabad. He can be reached at taha.ali@gmail.com

Published in Dawn, EOS, June 23rd, 2024

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HEALTH: IS YOGA ALL ITS CRACKED UP TO BE? - DAWN.com

In the ever-evolving landscape of machine learning and artificial intelligence (AI), large language models (LLMs) have emerged as powerful tools for a wide range of natural language processing (NLP) tasks, including code generation. Among these cutting-edge models, Code Llama 70B stands out as a true heavyweight, boasting an impressive 70 billion parameters. Developed by Meta and now available on Amazon SageMaker, this state-of-the-art LLM promises to revolutionize the way developers and data scientists approach coding tasks.

Code Llama 70B is a variant of the Code Llama foundation model (FM), a fine-tuned version of Metas renowned Llama 2 model. This massive language model is specifically designed for code generation and understanding, capable of generating code from natural language prompts or existing code snippets. With its 70 billion parameters, Code Llama 70B offers unparalleled performance and versatility, making it a game-changer in the world of AI-assisted coding.

Mixtral 8x7B is a state-of-the-art sparse mixture of experts (MoE) foundation model released by Mistral AI. It supports multiple use cases such as text summarization, classification, text generation, and code generation. It is an 8x model, which means it contains eight distinct groups of parameters. The model has about 45 billion total parameters and supports a context length of 32,000 tokens. MoE is a type of neural network architecture that consists of multiple experts where each expert is a neural network. In the context of transformer models, MoE replaces some feed-forward layers with sparse MoE layers. These layers have a certain number of experts, and a router network selects which experts process each token at each layer. MoE models enable more compute-efficient and faster inference compared to dense models.

Key features and capabilities of Code Llama 70B and Mixtral 8x7B include:

Amazon SageMaker, a fully managed machine learning service, provides a seamless integration with Code Llama 70B, enabling developers and data scientists to use its capabilities with just a few clicks. Heres how you can get started:

The following figure showcases how code generation can be done using the Llama and Mistral AI Models on SageMaker presented in this blog post.

You first deploy a SageMaker endpoint using an LLM from SageMaker JumpStart. For the examples presented in this article, you either deploy a Code Llama 70 B or a Mixtral 8x7B endpoint. After the endpoint has been deployed, you can use it to generate code with the prompts provided in this article and the associated notebook, or with your own prompts. After the code has been generated with the endpoint, you can use a notebook to test the code and its functionality.

In this section, you sign up for an AWS account and create an AWS Identity and Access Management (IAM) admin user.

If youre new to SageMaker, we recommend that you read What is Amazon SageMaker?.

Use the following hyperlinks to finish setting up the prerequisites for an AWS account and Sagemaker:

With the prerequisites complete, youre ready to continue.

The Mixtral 8x7B and Code Llama 70B models requires an ml.g5.48xlarge instance. SageMaker JumpStart provides a simplified way to access and deploy over 100 different open source and third-party foundation models. In order to deploy an endpoint using SageMaker JumpStart, you might need to request a service quota increase to access an ml.g5.48xlarge instance for endpoint use. You can request service quota increases through the AWS console, AWS Command Line Interface (AWS CLI), or API to allow access to those additional resources.

While Code Llama excels at generating simple functions and scripts, its capabilities extend far beyond that. The models can generate complex code for advanced applications, such as building neural networks for machine learning tasks. Lets explore an example of using Code Llama to create a neural network on SageMaker. Let us start with deploying the Code Llama Model through SageMaker JumpStart.

Additional details on deployment can be found in Code Llama 70B is now available in Amazon SageMaker JumpStart

Note: This blog post section contains code that was generated with the assistance of Code Llama70B powered by Amazon Sagemaker.

Let us walk through a code generation example with Code Llama 70B where you will generate a transformer model in python using Amazon SageMaker SDK.

Prompt:

Response:

Code Llama generates a Python script for training a Transformer model on the sample dataset using TensorFlow and Amazon SageMaker.

Code example: Create a new Python script (for example, code_llama_inference.py) and add the following code. Replace with the actual inference endpoint name provided by SageMaker JumpStart:

Save the script and run it:

python code_llama_inference.py

The script will send the provided prompt to the Code Llama 70B model deployed on SageMaker, and the models response will be printed to the output.

Example output:

Input

> Output

You can modify the prompt variable to request different code generation tasks or engage in natural language interactions with the model.

This example demonstrates how to deploy and interact with the Code Llama 70B model on SageMaker JumpStart using Python and the AWS SDK. Because the model might be prone to minor errors in generating the response output, make sure you run the code. Further, you can instruct the model to fact-check the output and refine the model response in order to fix any other unnecessary errors in the code. With this setup, you can leverage the powerful code generation capabilities of Code Llama 70B within your development workflows, streamlining the coding process and unlocking new levels of productivity. Lets take a look at some additional examples.

Lets walk through some other complex code generation scenarios. In the following sample, were running the script to generate a Deep Q reinforcement learning (RL) agent for playing the CartPole-v0 environment.

The following prompt was tested on Code Llama 70B to generate a Deep Q RL agent adept in playing CartPole-v0 environment.

Prompt:

Response: Code Llama generates a Python script for training a DQN agent on the CartPole-v1 environment using TensorFlow and Amazon SageMaker as showcased in our GitHub repository.

In this scenario, you will generate a sample python code for distributed machine learning training on Amazon SageMaker using Code Llama 70B.

Prompt:

Response: Code Llama generates a Python script for distributed training of a deep neural network on the ImageNet dataset using PyTorch and Amazon SageMaker. Additional details are available in our GitHub repository.

Compared to traditional LLMs, Mixtral 8x7B offers the advantage of faster decoding at the speed of a smaller, parameter-dense model despite containing more parameters. It also outperforms other open-access models on certain benchmarks and supports a longer context length.

Additional details on deployment can be found in Mixtral-8x7B is now available in Amazon SageMaker JumpStart.

Hyperparameters are external configuration variables that data scientists use to manage machine learning model training. Sometimes called model hyperparameters, the hyperparameters are manually set before training a model. Theyre different from parameters, which are internal parameters automatically derived during the learning process and not set by data scientists. Hyperparameters directly control model structure, function, and performance.

When you build complex machine learning systems like deep learning neural networks, exploring all the possible combinations is impractical. Hyperparameter tuning can accelerate your productivity by trying many variations of a model. It looks for the best model automatically by focusing on the most promising combinations of hyperparameter values within the ranges that you specify. To get good results, you must choose the right ranges to explore.

SageMaker automatic model tuning (AMT) finds the best version of a model by running many training jobs on your dataset. To do this, AMT uses the algorithm and ranges of hyperparameters that you specify. It then chooses the hyperparameter values that creates a model that performs the best, as measured by a metric that you choose.

Note: This blog post section contains code that was generated with the assistance of Mixtral 8X7B model, powered by Amazon Sagemaker.

Prompt:

Response:

There are instances where users need to convert code written in one programing language to another. This is known as a cross-language transformation task, and foundation models can help automate the process.

Prompt:

Response:

This Python code uses a built-in list data structure instead of the Java ArrayList class. The code above is more idiomatic and efficient in Python.

The AWS Cloud Development Kit (AWS CDK) is an open-source software development framework for defining cloud infrastructure as code with modern programming languages and deploying it through AWS CloudFormation.

The three-tier architecture pattern provides a general framework to ensure decoupled and independently scalable application components can be separately developed, managed, and maintained (often by distinct teams). A three-tier architecture is the most popular implementation of a multi-tier architecture and consists of a single presentation tier, logic tier, and data tier:

Prompt:

Response:

The following are some additional considerations when implementing these models:

Delete the model endpoints deployed using Amazon SageMaker for Code Llama and Mistral to avoid incurring any additional costs in your account.

Shut down any SageMaker Notebook instances that were created for deploying or running the examples showcased in this blog post to avoid any notebook instance costs associated with the account.

The combination of exceptional capabilities from foundation models like Code Llama 70B and Mixtral 8x7B and the powerful machine learning platform of Sagemaker, presents a unique opportunity for developers and data scientists to revolutionize their coding workflows. The cutting-edge capabilities of FMs empower customers to generate high-quality code, infill missing sections, and engage in natural language interactions, all while using the scalability, security, and compliance of AWS.

The examples highlighted in this blog post demonstrate these models advanced capabilities in generating complex code for various machine learning tasks, such as natural language processing, reinforcement learning, distributed training, and hyperparameter tuning, all tailored for deployment on SageMaker. Developers and data scientists can now streamline their workflows, accelerate development cycles, and unlock new levels of productivity in the AWS Cloud.

Embrace the future of AI-assisted coding and unlock new levels of productivity with Code Llama 70B and Mixtral 8x7B on Amazon SageMaker. Start your journey today and experience the transformative power of this groundbreaking language model.

Shikhar Kwatrais an AI/ML Solutions Architect at Amazon Web Services based in California. He has earned the title of one of the Youngest Indian Master Inventors with over 500 patents in the AI/ML and IoT domains. Shikhar aids in architecting, building, and maintaining cost-efficient, scalable cloud environments for the organization, and supports the GSI partners in building strategic industry solutions on AWS. Shikhar enjoys playing guitar, composing music, and practicing mindfulness in his spare time.

Jose Navarro is an AI/ML Solutions Architect at AWS based in Spain. Jose helps AWS customersfrom small startups to large enterprisesarchitect and take their end-to-end machine learning use cases to production. In his spare time, he loves to exercise, spend quality time with friends and family, and catch up on AI news and papers.

Farooq Sabiris a Senior Artificial Intelligence and Machine Learning Specialist Solutions Architect at AWS. He holds PhD and MS degrees in Electrical Engineering from the University of Texas at Austin and an MS in Computer Science from Georgia Institute of Technology. He has over 15 years of work experience and also likes to teach and mentor college students. At AWS, he helps customers formulate and solve their business problems in data science, machine learning, computer vision, artificial intelligence, numerical optimization, and related domains. Based in Dallas, Texas, he and his family love to travel and go on long road trips.

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Code generation using Code Llama 70B and Mixtral 8x7B on Amazon SageMaker | Amazon Web Services - AWS Blog