Category Archives: Artificial Intelligence

Artificial intelligence (AI) and machine learning (ML) are more than just buzzworthy terms for some cutting-edge companies. They are the foundations on which incredible businesses have been built. Even better, some of these companies make hay in industries essential to the economy.

Cybersecurity is top of mind for C-suite executives in all industries, government agencies, school districts, and even nonprofits. Cybercriminals are always on the prowl, costing organizations billions each year. IBM notes that up to 90% of cyberattacks and 70% of breaches come through endpoint devices.

AI-powered CrowdStrike Holdings (CRWD 0.30%) is the leader in endpoint security with a comprehensive, entirely cloud-based platform. The company's results are on fire, as I'll discuss below.

Meanwhile, data centers are crucial for cloud applications, data storage, computing power, and (definitely) complex AI and ML software that require massive computing power. Nvidia (NVDA -2.46%) is light-years ahead of its competition, and its data center software and hardware are mission critical. This is why its data center revenue rose 171% year over year last quarter to $10.32 billion.

CrowdStrike provides comprehensive security with its Falcon platform. The advantages are several: Falcon is cloud-native (no on-premises hardware required), customizable, and uses AI to analyze data and provide real-time protection.

The platform is modular, so customers can choose which modules they want or need. This plays into CrowdStrike's land-and-expand strategy: It gains a customer, proves the platform's worth, and then the customer adds more modules -- creating more revenue.

This shows up in the company's dollar-based net retention rate (DBNR), which has been above 120% dating back to the first quarter of fiscal 2019. DBNR measures the year-over-year increase in sales from an average customer. Above 100% is good, and above 120% is excellent.

You can probably guess how the chart of annual recurring revenue (ARR) growth looks:

Source: CrowdStrike.

The meteoric rise to $2.9 billion in ARR has enabled CrowdStrike to generate $416 million in free cash flow through the second quarter of this 2024 fiscal year and stack up $3.2 billion in cash against $742 million in long-term debt. Having cash on hand to fund growth is crucial in this environment, and the company likely won't have to borrow money at unfavorable interest rates.

CrowdStrike has a market cap near $50 billion, about 16 times Wall Street estimates for sales this fiscal year (which ends Jan. 31, 2024), and 13 times Wall Street estimates for the next fiscal year. That's not necessarily cheap (great companies usually aren't), but it is less than other growth companies like Snowflake and Palantir Technologies. In short, it's a great company, but consider dollar-cost averaging to take advantage of dips in the stock price along the way.

Nvidia is top of mind as investors await Tuesday's 2024 third-quarter earnings report. The stock is near another all-time high after a brief pullback recently. It has risen 237% so far in 2023 for a simple reason: Business is absolutely booming.

At the top, I mentioned the rise in data center sales, and this demand gives it pricing power in the industry. So profits and margins are soaring alongside revenue, as shown below.

NVDA operating income (quarterly) data by YCharts.

The company more than doubled operating revenue from the first quarter to the second this year, and its 50% margin is spectacular. Unfortunately, the secret is out, and the stock isn't cheap. Nvidia needs to continue raising Wall Street's estimate to maintain its valuation.

Nvidia's current price-to-earnings (P/E) ratio is 119, ridiculous on the surface. But the P/E is backward-looking -- it uses earnings that have already happened, while Wall Street is about the future. Based on the market's estimates for next year, the P/E falls to 45, then to 29 the following year. This is nearly identical to Microsoft's current valuation, as shown below.

NVDA PE ratio (forward 1 year) data by YCharts.

This tells me that the stock isn't tremendously expensive; however, Nvidia must push the envelope to give investors more juicy gains.

Bradley Guichard has positions in CrowdStrike and Nvidia and has the following options: long September 2024 $630 calls on Nvidia. The Motley Fool has positions in and recommends CrowdStrike, Nvidia, Palantir Technologies, and Snowflake. The Motley Fool recommends International Business Machines. The Motley Fool has a disclosure policy.

Excerpt from:

The Top 2 Artificial Intelligence (AI) Companies Revolutionizing the ... - The Motley Fool

BlackJack3D

There's more to AI than Nvidia. This is why the Robo Global Artificial Intelligence ETF (NYSEARCA:THNQ) is an interesting way for investors to access artificial intelligence and robotics. What I like about the fund is that it's very well diversified, making it a pure thematic play beyond the idiosyncratic aspect of just a select number of stocks in the space.

THNQ seeks to track the ROBO Global Artificial Intelligence Index. This index aims to track companies that generate a significant portion of their revenues from the AI market. THNQ offers a diversified strategy for investors, providing exposure to the very best public companies from around the globe, irrespective of their size and location. The fund serves as an excellent platform for investors to capitalize on the expected growth in AI and robotics, sectors that are transforming virtually every industry across the globe.

THNQ strives for a balance between growth and value, focusing on companies with strong business models and positive cash flows. The fund is rebalanced on a quarterly basis to ensure it consistently holds the most innovative and promising companies in the AI and robotics sectors. The ETF is not limited to technology companies; it also includes firms from the consumer cyclical, communication, and healthcare sectors, among others. Geographically, THNQ's holdings are diverse, with significant exposure to non-U.S. markets, particularly in Europe and East Asia.

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I mentioned at the start of the writing that this is a well-diversified fund. No holding currently makes up more than 2.71% of the fund, and crowd favorite Nvidia is currently ranked 8 in the top 10. Again - I very much like this. It makes this a far more nuanced and diversified way of playing AI than other products where Nvidia is the biggest holding.

roboglobaletfs.com

The sector composition of THNQ is diverse, ensuring that investors gain exposure to various segments of the economy. As you'd expect, the majority of the fund is in the Technology space, but it's not a pure tech fund in that it does have stocks from non-tech groups.

ycharts.com

When comparing THNQ to its peers, it's important to note that it outperformed the TrueShares Technology, AI & Deep Learning ETF (LRNZ) which is another fund in the space. LRNZ is considerably less diversified with top holdings having more concentration risk than THNQ.

stockcharts.com

Investing in AI and robotics comes with its share of advantages and potential risks.

On the positive side, these sectors are experiencing unprecedented growth due to advancements in technology and increasing adoption across different industries worldwide. Furthermore, AI and robotics offer substantial potential for innovation, making them attractive sectors for forward-thinking investors.

On the flip side, investing in AI and robotics also carries risks. These include regulatory uncertainties, privacy concerns, and potential job displacements due to automation. Moreover, these sectors are highly competitive, requiring constant innovation and substantial capital expenditure.

Personally, I think THNQ is a solid avenue for investors looking to gain exposure to the rapidly growing fields of AI and robotics. It's well diversified, and if you believe that AI is unstoppable, despite relatively weak performance as of late, this becomes a good fund to access the trend.

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Robo Global Artificial Intelligence ETF: A Diversified AI Play (THNQ) - Seeking Alpha

One activation method that opens up a great many possibilities for different functional groups, at least on paper, is borylation. In this process, a chemical group containing the element boron is bonded to a carbon atom in the scaffold. The boron group can then simply be replaced by a whole range of medically effective groups.

"Although borylation has great potential, the reaction is difficult to control in the lab. That's why our comprehensive search of the worldwide literature only turned up just over 1,700 scientific papers on the subject," Atz says, describing the starting point for his work.

The idea was to take the reactions described in the scientific literature and use them to train an AI model, which the research team could then use to consider new molecules and identify as many sites as possible on them where borylation would be feasible. However, the researchers ultimately fed their model only a fraction of the literature they found. To ensure that the model wasn't misled by false results from careless research, the team limited itself to 38 particularly trustworthy papers. These described a total of 1,380 borylation reactions.

To expand the training dataset, the team supplemented the literature results with evaluations of 1,000 reactions carried out in the automated laboratory operated by Roche's medicinal chemistry research department. This allows many chemical reactions to be carried out at the milligram scale and analysed simultaneously. "Combining laboratory automation with AI has enormous potential to greatly increase efficiency in chemical synthesis and improve sustainability at the same time," says David Nippa, a doctoral student from Roche who accomplished the project together with Atz.

The predictive capabilities of the model generated from this data pool were verified using six known drug molecules. In five out of six cases, experimental testing in the laboratory confirmed the predicted additional sites. The model was just as reliable when it came to identifying sites on the scaffold where activation isn't possible. What's more, it determined the optimum conditions for the activation reactions.

See the article here:

Artificial intelligence finds ways to develop new drugs - Mirage News

JPMorgans asset management division employs 1300 portfolio managers and research analysts globally across stocks, bonds, derivatives and alternative asset classes, with $US400 million a year invested into technology to leverage AI and machine learning models.

That hasnt stopped JPMorgans rivals from also investing, with a PwC global asset management survey showing that more than 90 per cent of investment firms are already using disruptive technology such as AI within their business.

On AI, we decided a long time ago to build our own tech and not rent someone elses, Mr Gatch said. I think thats now a competitive advantage for our portfolio managers and clients.

The global market for AI in asset management is estimated to be worth about $US2.6 billion in 2022 and is expected to expand at a compound annual growth rate of 24.5 per cent over the next seven years, according to one estimate by US firm Grand View Research.

Earlier this year BlackRock, the worlds largest asset manager, described AI in its mid-year investment outlook as a mega force trend that would help unlock the value of the data gold mine some companies may be sitting on.

Elsewhere, JPMorgan is among a slew of investment banks, including Macquarie, that are launching more ETF products into the Australian market to challenge the dominance of BlackRock, Vanguard and State Street.

Mr Gatch said the global ETF industry had attracted $US1.4 trillion of funds in 2022, versus $US700 billion for traditional investment funds unlisted on exchanges.

In Australia, data from Betashares shows the local ETF industry recorded $13.5 billion of net inflows last year, versus outflows of $26.8 billion from unlisted managed funds.

JPMorgans strategy to grab market share is to offer so-called active ETFs that may outperform the traditional index-tracking products. The firm forecasts active ETF inflows to grow at a 40 per cent annual rate for the next five years, compared to 17 per cent for indexed ETFs.

That has big implications for how we run our business and whats happening is fairly astonishing, Mr Gatch added.

The story is no longer about indexing, we think that [active] will drive a wave of dramatic growth into the industry.

The JPMorgan Equity Premium Income Product is said to have attracted more inflows than any other ETF in 2023, with net assets of around $US29 billion in November. Much of its popularity is due to an active strategy of selling call options that provide extra income if the market falls or tracks sideways.

On fees, the CEO said asset managers, including the ETF sector, remained under pressure, but JPMorgans investment in digital currency and blockchain technology could eventually bring costs down.

Were doing a bunch of stuff on blockchain, he said. We have working groups looking at tokenisation for private markets, how to improve liquidity between institutions for private credit. Its going to take time, but weve got smart people, and operational efficiency is important.

As for JPMorgan launching a bitcoin ETF, its not on the agenda anytime soon. No, weve not done [regulatory] filings.

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JPMorgan's George Gatch says artificial intelligence gives them firm ... - The Australian Financial Review

HyperCycle is a decentralized network that connects AI machines to make them smarter and more profitable. It enables companies of all sizes to participate in the emerging AI computing economy.

Artificial intelligence (AI) is a rapidly evolving field that seems likely to fall into the hands of major companies or organizations with nationally driven budgets. One might think that only these have the massive financial resources to generate the computing power to train and ultimately own AI.

Recent events at OpenAI, a developer of the AI chatbot ChatGPT, highlight the challenges of centralized AI development. The firing of CEO Sam Altman and the resignation of co-founder Greg Brockman raise questions about governance and decision-making in centralized AI entities and highlight the need for a more decentralized approach. Srinivasan Balaji, a former chief technology officer at Coinbase, has become a staunch proponent for increased transparency in the realm of AI, advocating for the adoption of decentralized AI systems.

In addition to centralization, theres a lot of fragmentation in the AI space, meaning cutting-edge systems are unable to communicate with one another. Moreover, a high degree of centralization brings considerable security risks and reliability issues. Plus, given the vast amounts of computing power needed, efficiency and speed are key.

To achieve the full potential of AI that answers to all of humanity, we need a different approach one that decentralizes AI and allows AI systems to communicate with each other, eliminating the need for intermediaries. This would increase AI systems time to market, intelligence and profitability. While many systems are currently specialized in specific tasks, such as voice or facial recognition, a future shift to artificial general intelligence could allow one system to undertake a wide range of tasks simultaneously by delegating those tasks to multiple AIs.

As mentioned above, currently, the AI industry is dominated by large corporations and institutional investors, making it difficult for individuals to participate. HyperCycle, a novel ledgerless blockchain architecture, emerges as a transformative solution, aiming to democratize AI by establishing a fast and secure network that empowers everyone, from large enterprises to individuals, to contribute to AI computing.

HyperCycle is powered by a layer 0++ blockchain technology that enables rapid, cost-effective microtransactions between diverse AI agents interconnected to each other, and collectively solving problems.

This internet of AIs allows systems to interact and collaborate directly without intermediaries. It addresses the challenges of overcoming the slow, costly processes of the siloed AI landscape.

This is particularly timely, as the number of machine-to-machine (M2M) connections globally is increasing rapidly.

For instance, existing companies could interact with HyperCycles AIs specializing in IoT, blockchain, and supply chain management to optimize logistics for clients, predict maintenance before breakdowns occur, and ensure seamless data integrity. By enabling this interconnected ecosystem of decentralized A Is, HyperCycle can lead to operational efficiency and innovation in service offerings.

HyperCycle has also partnered with Penguin Digital to create HyperPG, a service that connects all the network beneficiaries together. HyperPG uses Paraguays abundant hydropower to provide a green and efficient source of energy for AI computing.

One of HyperCycles key features is the HyperAiBox, a plug-and-play device that allows individuals and organizations to perform AI computations at home and reduces their reliance on large corporations with vast data centers. The compact box is about the size of a modem, has a touchscreen, and allows nodes to be operated from home and network participants to be compensated for the resources they provide to the network. It is also a low-power solution.

The launch of HyperCycles mainnet, ahead of schedule, highlights the networks rapid growth. Currently, over 59,000 initial nodes are providing Uptime to the network by covering operational expenses. An additional 230,000 single licenses will soon join the ecosystem. This expansion indicates a strong demand for over 295 million HyPC tokens, reflecting the networks engagement and growth.

The three key metrics of Uptime, Computation, and Reputation incentivize node operators to maintain high standards, ensuring a stable, secure, and decentralized network environment.

Since June 2023, HyperCycles network has been operational, scaling up as demand increases. Source: HyperCycle

AI remains at a nascent stage, but HyperCycles goal is to anticipate the challenges that might stand in this technologys way and break down barriers to entry, making AI more accessible and affordable to everyone.

Disclaimer. Cointelegraph does not endorse any content or product on this page. While we aim at providing you with all important information that we could obtain in this sponsored article, readers should do their own research before taking any actions related to the company and carry full responsibility for their decisions, nor can this article be considered as investment advice.

Originally posted here:

How an 'internet of AIs' will take artificial intelligence to the next level - Cointelegraph

Artificial Intelligence (AI) and automation have had a profound impact on the field of engineering. These technologies have the potential to revolutionise the way engineers design, analyse, and optimise systems and processes. However, it's important to note that while AI and automation offer tremendous benefits, they also come with challenges, including ethical considerations, job displacement concerns, and the need for robust cyber security. Engineers and organisations need to carefully plan and implement these technologies to maximise their advantages while addressing potential drawbacks.

The emergence of Artificial Intelligence (AI) in engineering has been a transformative development that has significantly impacted various engineering fields. AI technologies, such as machine learning and deep learning, have been applied to engineering tasks to enhance efficiency, accuracy, and innovation. Machine learning will be among the top engineering talents in demand in 2022. Learning how to incorporate AI into processes is already in demand due to engineers' extraordinary capacity for solving complicated challenges.

90% of major corporations are thought to have made some kind of investment in artificial intelligence (AI) technologies. Less than 15% of these firms, however, are currently using AI in their working environment. AI is one of the technologies with the quickest growth in the engineering industry. Even while there has always been some worry that AI may eliminate some occupations, the current state of affairs is that new technology is creating a variety of chances for engineering skills.

AI is used in manufacturing at many phases of the production process to increase productivity, accuracy, and automation. It uses machine learning, data analysis, and algorithms to let robots do tasks that once required direct human interaction. Utilising characteristics like predictive maintenance, quality control, process enhancement, and others, this technology boosts output and decreases downtime. By analysing vast amounts of data in real-time, AI-driven systems can make sensible decisions, optimise processes, and identify trendspeople would miss.

AI and automation have significant advantages for businesses since they can increase production, efficiency, and financial performance. Enhanced Productivity Enhanced Efficiency, Improved Data Analysis and More favourable bottom-line results are some major advantages. However,there are also many challenges in developing and using AI for automotive electronics, including complexity, dependability, security, and regulation.

AI algorithms can be used to analyse sensor data from structures, such as temperature and vibration data, to forecast when maintenance is necessary,and to spot warning indications of structural breakdown before they materialise. AI-powered cameras are used for inspection and surveillance. Artificial intelligence-driven structural analysis systems may simulate and assess complicated structural behaviour, assisting engineers in locating possible weak points, foretelling failure modes, and improving structural performance. By quickly adapting to user preferences, AI-powered optimisation attempts to improve the personalisation, cost-effectiveness, and utility of digital experiences. With the help of this technology, organisations can make data-driven decisions that enhance the functionality of their websites, user engagement, and rates of conversion.

Machine Learning (ML) optimises energy efficiency models, predicting the consumption of energy tools. Over the last five years, ML techniques gained traction in designing energy-efficient systems amid rising demand for technologies like smart buildings and IOTs (Internet of Things). Sustainable growth in smart cities relies on technological advancements, merging sustainability with energy efficiency. Artificial Intelligence (AI) plays a vital role in managing, coordinating, and forecasting electricity supply. The global push for a low-carbon transition amplifies the significance of AI in achieving energy goals. AI-driven "smart consumption" transforms energy usage patterns, enabling decentralised power grids for balanced energy flows.

Systems with artificial intelligence (AI) can be used to identify and detect traffic events such asaccidents, wrong-way driving, speeding, or roadblocks. Real-time traffic data is analysed using AI from a variety of cameras and IoT devices, including cars, buses, and even trains. As over 90% of accidents are the result of human error, it is anticipated to drastically reduce the number of accidents. AVs (Autonomous Vehicles) can lower the cost of travel. For instance, AVs will save labour expenses when used in public transportation. With smart carpooling, costs can be further reduced. By removing the need for human drivers, a driverless car might significantly ease traffic congestion. This may lead to a significant increase in car sharing, which would reduce the number of vehicles on the road and the overall carbon footprint compared to more conventional modes of transportation.

Integrating AI and automation into engineering processes offers numerous benefits, such as increased efficiency, improved accuracy, and cost reduction. However, it also presents several challenges and ethical considerations like safety and reliability, Algorithm Complexity, Human-AI Collaboration, Integration with Existing Systems and Ethical Considerations (data collection, privacy, and decision-making). Concerns about job displacement and human-AI collaboration have been growing as artificial intelligence and automation technologies continue to advance. These concerns centre on the potential for AI and automation to replace human workers in various industries, leading to job loss and economic disruption. However, it's important to note that these concerns are not without nuance, and there are also opportunities for collaboration between humans and AI that can lead to more productive and fulfilling work environments.

Digital twins, virtual replicas of physical entities, leverage real-time data, simulation, analytics, and visualisation. Enhancing decision-making, they cut costs and boost efficiency. Manufacturers benefit by integrating digital twins seamlessly, reducing expenses and accelerating value. Architects and engineers employ digital twins in building design, incorporating details on use, materials, and maintenance. This streamlines construction oversight and communication, ensuring better quality.

AI-driven maintenance prediction is transforming asset management, using historical data and real-time analysis to predict equipment failures and facilitate proactive maintenance. By identifying flaws and analysing behavioural patterns, AI recommends optimal times for replacements or repairs, reducing emergency repairs. In various industries, AI enhances data analytics, offering valuable insights into market trends, client preferences, and business strategies. AI-generated maintenance schedules prevent over-maintenance and minimise breakdowns, conserving resources. For example, AI monitors machinery spindles in milling operations, reducing the need for costly repairs. This innovative approach optimises efficiency and minimises wasteful spending.

AI systems face risks such as adversarial machine learning attacks, where attackers manipulate input data to alter the model's output, potentially leading to poor decision-making and security vulnerabilities. Privacy concerns revolve around the increased likelihood of data breaches and unauthorized access to personal information. With the vast amount of data being collected, there's a risk of misuse through hacking or security flaws. Organizations must regularly assess their infrastructure's security, identify vulnerabilities, and prioritize corrections to safeguard against cyber threats. This involves timely application of security patches, software and hardware updates, and implementation of robust security configurations.

Designing AI systems with human factors in mind is crucial to ensure usability, safety, and user acceptance. User experience considerations play a pivotal role in AI-integrated engineering solutions, as they directly impact how users interact with and perceive AI technologies. It not only improves the usability, safety, and acceptance of AI but also helps avoid potential pitfalls and negative consequences associated with poorly designed systems. By prioritising user experience considerations, AI engineers can create solutions that are not only technically proficient but also genuinely beneficial and user-friendly. Designing AI systems that complement human capabilities rather than replace them is very significant. When users see AI as a helpful tool that enhances their work, they are more likely to accept and use it.

In the past, it was believed that AI would eventually displace workers. Organisations observe that AI is expanding export opportunities and building a highly qualified workforce. Additionally, as automation and AI can now finish the fundamental and monotonous duties, engineering roles can concentrate more on activities that bring value, making engineering employment much more dynamic and fulfilling.

The future advancements of AI and automation in engineering hold immense promise, with several exciting trends and developments on the horizon like aerospace, electronics, energy storage, etc. Quantum algorithms can also be used for tasks like molecular modelling, optimising supply chains, and solving complex equations in real time. Generative design, powered by AI, is transforming how engineers approach product design. This can lead to highly efficient and innovative designs in various industries, including automotive and architecture. We can expect to see more autonomous drones, self-driving vehicles, and robotic systems in manufacturing and logistics. These technologies will improve efficiency, safety, and precision in various engineering applications.

It's important to note that while these advancements offer numerous benefits, they also come with challenges, including ethical concerns, cyber security risks, and the need for up-skilling the workforce. Engineers and organisations should stay informed and adapt to these emerging technologies to harness their full potential while addressing associated challenges.

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Artificial Intelligence and Automation in Engineering - Drishti IAS