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Unveiling the Top AI Development Technologies | by Pratik … – DataDrivenInvestor

October 16, 2023 | Author: admin

With the help of cutting-edge technologies, artificial intelligence is transforming today drastically. Artificial intelligence, once limited to being a distinct field of study for three decades, has expanded its reach to encompass various applications across various domains. According to Grand View Research, AI will continue transforming many industries, with a projected annual growth rate of 37.3% between 2023 and 2030. This rapid rise will highlight the future significance of AI technology.

Today, we can see quite a range of emerging AI technologies. From small businesses to enormous corporations, there is a race to adopt artificial intelligence for data mining, operational excellence, etc. Lets talk about the most recent Artificial Intelligence developments.

Machine Learning is yet another useful technology in the Artificial Intelligence domain. This technology focuses on training a machine (computer) to learn and think independently. Machine Learning typically uses many complex algorithms for training the machine.

The machine is given a set of categorized or uncategorized training data about a specific or public domain during the process. The machine then analyses the data, draws inferences, and stores them for future use. When the machine encounters any other sample data of the domain it has already learned, it uses the stored inferences to draw necessary conclusions and respond appropriately.

A flexible and robust open-source machine learning framework that offers a comprehensive ecosystem for constructing and implementing ML models, strongly emphasizing deep learning and versatile architecture.

A popular open-source machine learning framework that emphasizes dynamic computation graphs, making it suitable for research and prototyping, with strong support for neural networks and deep learning.

A high-level neural networks API that runs on top of TensorFlow, PyTorch, or other frameworks simplifies building and training deep learning models, particularly for beginners and rapid prototyping.

Communicating effectively and clearly can be challenging, but processing information for machines differs from the human brain. Natural Language Generation (NLG) is crucial in converting text into data, enabling systems to convey ideas and thoughts. NLG finds extensive applications in customer service, generating reports, and producing market summaries.

Prominent companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop offer NLG solutions. It comes as no surprise that NLG is among the top 15 cutting-edge artificial intelligence technologies.

A comprehensive library for NLP tasks, providing tools for tokenization, stemming, tagging, parsing, and more.

A popular NLP library offering efficient tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and pre-trained word vectors.

A library for topic modeling, document similarity analysis, and unsupervised learning of word embeddings like Word2Vec and FastText.

A Java-based NLP library by Stanford provides many tools, including tokenization, part-of-speech tagging, parsing, and sentiment analysis.

A simple and user-friendly library built on NLTK, offering tools for tokenization, part-of-speech tagging, noun phrase extraction, and sentiment analysis.

A library for state-of-the-art transformer models like BERT, GPT, and XLNet, enabling tasks such as text classification, named entity recognition, and question answering.

A powerful library built on PyTorch, specifically designed for NLP research, providing high-level abstractions for building and evaluating deep learning models.

The corporate landscape is experiencing a remarkable upswing in the need for artificial intelligence (AI) software. However, as the significance of such software grows, the need for compatible hardware also becomes apparent. Traditional chips cannot adequately support AI models, leading to a new generation of AI chip development designed for neural networks, deep learning, and computer vision tasks.

These AI solutions encompass a range of components, including CPUs capable of handling scalable workloads, specialized silicon chips built for neural networks, and innovative neuromorphic chips. Major technology organizations like Nvidia, Qualcomm, and AMD are actively involved in creating advanced chips that can perform complex AI calculations.

A popular computer vision library that provides many tools and algorithms for image and video processing, object detection, feature extraction, and more.

An open-source machine learning framework with a powerful computer vision module, TensorFlow Object Detection API, for training and deploying object detection models.

Another popular deep learning framework that offers computer vision capabilities is through its TorchVision library, which provides tools for image classification, object detection, and semantic segmentation.

A Python library focusing on image processing tasks, offering a comprehensive collection of algorithms and functions for image enhancement, filtering, segmentation, and feature extraction.

A C++ library with Python bindings specializing in facial detection and recognition, providing pre-trained models for face detection, landmark detection, and face alignment.

A deep learning framework known for its efficiency and speed, Caffe includes a computer vision library that supports image classification, object detection, and semantic segmentation.

A user-friendly computer vision library designed for beginners, SimpleCV provides easy-to-use functions for basic image processing tasks, such as filtering, feature detection, and color tracking.

A scientific computing library for Python, SciPy includes modules for image processing that offer functions for tasks like filtering, morphological operations, image restoration, and mathematical transformations.

Many businesses need help utilizing AI, primarily due to the high costs associated with the in-house development of AI products. Consequently, there is a growing demand for outsourced AI solutions, as they offer a more cost-effective approach for small and medium-sized businesses and budget-conscious large enterprises to dip their toes into AI. By leveraging cloud-based AI services, organizations can access the benefits of artificial intelligence without the hefty investment typically required for in-house development.

Amazons AI initiatives include enhancing its consumer devices like Alexa and delivering services through AWS. Interestingly, a significant portion of AWSs business cloud services is built upon the foundation of these consumer products. As Alexa evolves and improves, its business equivalent will follow suit.

Amazon Lex offers a comprehensive solution for integrating conversational interfaces into any application. This technology is currently utilized in Alexa, empowering developers to design chatbots with advanced natural language capabilities.

With its unique hardware innovation called the Tensor Processing Unit (TPU), Google sets itself apart from other cloud providers. The TPU is a specialized chip specifically designed to enhance the performance of TensorFlow, Googles open-source machine learning platform.

While other major cloud providers offer TensorFlow, none can access TPUs, giving Google a competitive edge. TPUs boast remarkable speed improvements, with 15 to 30 times faster performance than traditional CPUs or GPUs. They deliver up to 180 teraflops of computing power, making complex machine-learning tasks significantly faster and more efficient.

In addition to its hardware advantage, Google leverages its AI capabilities from consumer-facing products to cater to business users. The powerful AI algorithms that drive Google applications like Images, Translate, Inbox (Smart Reply), and voice search in Android are accessible through Google Compute Engine, its cloud offering.

This means businesses can harness the same cutting-edge AI technology that powers Googles popular consumer applications to improve their operations and services.

Microsoft organizes its AI solutions into three categories: AI Services, AI Tools and Frameworks, and AI Infrastructure. Unlike Amazon, Microsoft also leverages some of its consumer products to build its business AI offerings.

Under the AI Services category, there are three subgroups. The first is pre-built AI capabilities, which enhance customer-facing applications like web chatbots. Cognitive Search combines Azure Search with Cognitive Services to provide advanced search capabilities. Conversational AI utilizes Azure Bot Service to enable conversational bots with enhanced features like richer dialogs, full personality customization, and voice customization.

Artificial Intelligence (AI) encompasses computational models that replicate intelligence.

The widespread adoption of AI across various sectors has already yielded many benefits. However, it is crucial for organizations implementing AI to conduct rigorous pre-release trials to identify and mitigate biases and errors. The design and models employed should be robust and capable of withstanding real-world challenges.

Organizations should establish and uphold standards while hiring experts from diverse disciplines to facilitate informed decision-making. AIs ultimate aim and future vision revolve around automating complex human activities and eradicating errors and biases.

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Bring on AI and Machine Learning to take on escalating cybercrime threats – Gulf News

October 16, 2023 | Author: admin

Tech breakthroughs have become prevalent in this digitised environment, and revolutionising the facets of our daily lives. The growth of AI has been a spectacular breakthrough, ushering in possibilities and transformations.

AI applications have found their way into industries, allowing for automation, better decision-making, and overall efficiency. However, as the world embraces digitalisation and AI, it is also confronted with the darker side of this technological revolution - the new perils of cybercrime.

Particularly in the aftermath of COVID-19, the travel industry has seen tremendous expansion and appeal. The unprecedented rebound in outbound travel has increased the exposure to cybercriminals. The travel and tourist industry has become a target of cyberattacks, suffering a variety of threats such as data breaches, ransomware attacks, and phishing efforts.

According to industry forecasts, the costs of cyberattacks would climb by a steep 15 per cent every year, reaching a staggering $10.5 trillion by 2025. This shows the intensity of the situation, emphasising the critical need for organisations to commit significant resources to strengthen their cybersecurity.

Recognising the importance, an astounding 85 per cent of SMEs have stated their intention to boost IT security investment by end-2023. That explains why investing in comprehensive cybersecurity measures is not only a must, but a strategic imperative for long-term success.

Following are a few technology advances that can help combat cybercrime:

AI and Machine Learning

AI and ML are revolutionising the field of cybersecurity by providing advanced capabilities to detect, analyze, and respond to cyber threats in real-time. These enable systems to automatically learn from vast amounts of data and identify patterns, anomalies, and potential risks that might be difficult for human operators to detect.

AI and ML algorithms can analyse network traffic, user behavior, and system logs to identify malicious activities, such as malware infections, unauthorised access attempts, or abnormal data transfers. This approach allows organisations to respond swiftly and effectively, minimising the impact of cyberattacks.

Blockchain

Blockchain technology, originally developed for cryptocurrencies like Bitcoin, offers a decentralised and tamper-resistant method of storing and verifying data. Its inherent properties, such as transparency, immutability, and consensus-based validation, make it highly suitable for enhancing cybersecurity.

In the context of cybersecurity, blockchain can be used to secure critical data, authenticate identities, and establish secure communication channels. By decentralising data storage and ensuring that information cannot be easily altered, blockchain technology adds an extra layer of protection against unauthorised access, data tampering, and insider threats.

Zero Trust architecture

Traditional network security models operate on the assumption that once a user or device gains access to the internal network, they can be trusted. However, with the increasing sophistication of cyber threats, the Zero Trust architecture has gained prominence.

Zero Trust revolves around the concept of never trust, always verify. Under this model, all users, devices, and network traffic are treated as potentially untrusted and are continuously verified and authenticated before being granted access to critical resources.

Zero Trust employs various security measures such as multi-factor authentication, strict access controls, and continuous monitoring to ensure that only authorized entities can access sensitive data and systems. By implementing Zero Trust principles, organisations can mitigate the risk of internal and external attacks, limit lateral movement, and protect their digital assets.

Staying ahead of cyber threats requires organisations to embrace evolving technologies as integral components of their cybersecurity strategies. These technologies have helped many organisations in the industry stay safe while also lowering their security management expenditures.

As businesses continue to grapple with the growing challenges of cybersecurity, those at the forefront of technological innovation will gain a competitive advantage by safeguarding their operations, reputation, and, most importantly, their invaluable data.

Suraj Tiwari

The writer is Head - Information Security, VFS Global.

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Redefining education in the AI era: the rise of generalists – asianews.network

October 16, 2023 | Author: admin

October 16, 2023

KUALA LUMPUR Since the emergence of ChatGPT, many have been concerned about its potential negative impacts. Debates have sprung up, and tools have been developed to detect if assignments were crafted with the help of ChatGPT.

This concern is understandable. For instance, even in a notoriously difficult statistical machine learning course I taught last semester, ChatGPT could easily tackle it. I tested it with my final exam questions, and it outperformed nearly 90% of my students.

However, I dont think we should prohibit students from using such tools.

Is AI-generated content plagiarism?

That is to be debated. But to start, implementing such a ban is almost impossible due to the nature of machine learning.

Machine learning is fundamentally different from our typical internet searches. It efficiently extracts inherent relationships in data.

When AI generates content, it randomly samples based on these relationships rather than retrieving the most matching content. Therefore, the output cant strictly be called plagiarism as it doesnt specifically copy from any particular dataset, just like an impromptu speech might revolve around the same ideas but will never be identical each time.

So, in my classes, I not only dont prevent students from using ChatGPT, but I also encourage them to embrace it. As my colleague put it, its quite impolite not to.

Redefining the educational model

Should we be worried that students will stop learning?

While these tools will definitely revolutionize our teaching methods, I see it as a positive change. Just like I became reliant on Google Maps, it allows me to invest my time and energy into honing other skills.

Undoubtedly, traditional rote-learning methods are most at risk, especially when faced with advanced language models.

While the future of educational approaches remains uncertain, Im convinced that the new methods will prioritize problem-solving skills over rote memorization.

Typical assignments, like multiple-choice questions, are too easy for these large language models. I prefer assigning research-based projects, which reflect students genuine capabilities.

While current large-scale language models do have reasoning abilities, they still somewhat lack lateral thinking. If students can guide AI models based on their learning, the quality of the solutions achieved with AI assistance will significantly improve.

In this era, learning how to harness AI tools effectively is essential for everyone.

Leading universities like Tsinghua and Harvard now offer foundational courses for non-computer science majors. These courses teach them how to use tools like ChatGPT, delve into the principles behind them, and how to tweak these models for specific needs.

Even in my recent astrophysics class, most of my students werent computer science majors, but I still dedicated a session to introduce them to the nuances of large-scale language models.

The rise of the generalists

While machine learning is all the rage, and computer science enrollments are skyrocketing, Im often asked about major selection advice. Being at the intersection of astrophysics and computer science, I have some thoughts to share.

For those genuinely passionate about computing, a computer science degree is great. But, understand that the curriculum encompasses much more than just machine learning, including traditional system designs and compilers. As the use of large language models becomes commonplace, interdisciplinary knowledge is crucial. For instance, without knowledge in astrophysics, utilizing these models to extract valuable insights from large language models becomes challenging.

Interestingly, many leaders in the machine learning community dont come from a computer science background. For instance, some researchers at Anthropic, a main competitor to OpenAI, hail from physics backgrounds. Experts from varied academic fields bring unique perspectives and often introduce innovative viewpoints.

Breaking the alienation of the individual

We are in an era where generalists are emerging as leaders. Only by understanding and integrating specific application scenarios can the true value of these tools be fully realized. And I believe this is the greatest gift machine learning offers.

Looking back at the Industrial Revolution, individuals were often seen as mere cogs in the vast machinery of society, repetitively performing the same tasks. This cog-in-the-machine approach meant anyone could easily be replaced, ensuring continuous societal functioning. However, this often led to dehumanization.

Now, as machine learning capabilities show, repetitive tasks are likely the first to be automated, whether they are low-level or high-end specialized tasks.

Although jobs will undergo major transformations, and there are genuine concerns, those who will excel in this tech-driven era will be the generalists who thrive in multiple domains, rather than isolated specialists.

In this era of rapid change and competition, the accessibility of machine learning (as Ive mentioned in previous writings) has made global competition unprecedentedly open.

Its no longer just about the race between superpowers (unlike the nuclear arms race). Only nations that nurture a vast number of interdisciplinary experts will stand out.

So, is the Malaysian educational system ready?

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How the Human-Machine Intelligence Partnership Is Evolving – AiThority

October 16, 2023 | Author: admin

AI has enjoyed a long hype cycle, recently reignited by the introduction and rapid adoption of OpenAIs ChatGPT. Companies are now at varying stages of AI adoption given their business goals, resources, access to expertise, and the fact that AI is being embedded in more applications and services. Irrespective of industry, AI depends on a critical mass of quality data. However, the necessary quality depends on the use case. For example, as consumers, weve all been the victim of bad data as evidenced by marketing promotions we either laugh at or delete.

In the scientific community, such as the pharmaceutical and life sciences industry, bad data can be life-threatening, so data quality must be very high.

Also, unlike many other industries, the data required to discover a novel drug molecule tends to be scarce rather than abundant.

The data scarcity prevalent in the pharmaceutical and life science industries promotes a stronger alliance between humans and machines. Over time, there has been a significant accumulation of scientific data, the understanding of which demands a high level of education. This data accrual has been quite costly, leading to a general reluctance among owners to share the information they have acquired.

The intricate nature of scientific data implies that only scientists within the same field can comprehend the deeper contexts. Therefore, the volume of data available in an appropriate context is typically limited. This scarcity makes it challenging to develop credible AI algorithms in the healthcare industry.

To counteract this data deficit, human experts play an essential role in providing context and supplementary information. This human intervention helps in the co-development of algorithms and the workflows in which these algorithms are accurately utilized.

AI hype cycles have caused fear, uncertainty, and doubt because vendors are underscoring the need for automation in white-collar settings. In the distant past, AI was firmly focused on production-line jobs impacting blue-collar workers. Back then, no one anticipated AI would impact knowledge work, especially because AI capabilities were limited to the technology of the day and in most cases, it was rule-based.

Now, we see pervasive use of AI techniques such as machine learning and deep learning that can analyze massive amounts of data at scale. Instead of following a deterministic set of rules, modern systems are probabilistic, which makes things like prediction, as opposed to just historical data analysis, possible.

In the case of pharmaceuticals and life sciences, its possible to import tags from research papers, which is helpful, but the context tends not to be stated explicitly, so scientists need to help AI understand the underlying hypothesis or scientific context. The system then learns by being rewarded for good outcomes and scientists rejecting the bad outcomes. Without a human overseeing what AI does, it can drift in a manner that makes it less accurate.

In fact, it can take several weeks or months to create molecules and test them under experimental conditions. If animals are involved, the process could take years.

Some scientists or professionals dont want to share their work with AI, particularly when they are highly educated and experienced. These people know how long research takes and how expensive it can be, and theyve grown comfortable with that over time.

Also, the pharmaceutical and life sciences industries are highly regulated, so irrespective of whether AI is used or not, there are certain processes, and certain levels of rigor required just to ensure patient safety.

Interestingly, when well-trained scientists see AI in action, it becomes abundantly clear that it can handle a million or more data points easier and faster than a human. Suddenly, it becomes clearer that AI is a valuable tool that can save time and money and enable greater precision.

However, that doesnt mean that scientists trust what they see, especially when it comes to deep learning, which includes large language models such as ChatGPT. The problem with deep learning is that it tends to be opaque, meaning that it can take an input or multiple inputs and produce a result, but the AI is unable to explain in terms understandable to humans how it arrived at that result.

Thats why theres been a loud cry for AI transparency; people want to trust the result. Scientists and auditors demand it.

One of the biggest genetic databases is 23andMe. This is the gene testing service that reveals a persons ethnicity. It has also enabled individuals to discover family members they never met, such as a set of twins, one of whom was adopted. The service offers significant entertainment value.

However, from a scientific standpoint, it doesnt offer much.

Without understanding someones medical history, understanding genetic composition can only be somewhat helpful. As an example, a brother and sister may carry the same gene that is expressed in one and dormant in the other.

The more we know about an individual, the better chance there is of choosing the compounds that will work for them and at what dosage level. Today, theres still a lot of trial and error, and doses are standardized. In short, AI will help make personalized medicine possible.

The pharmaceutical and life sciences industries are both highly competitive. About two years ago, I visited Cambridge University and noticed that big pharma companies had sent a researcher or two to learn about Cambridges experimental automation technology that utilizes AI. Big pharma companies often work with research institutions to learn about scientific discovery and to get the high-quality data they need.

Another example is Recursion Pharmaceuticals where theyre automating biology-related processes. They photograph cells after treating some molecules and then use AI algorithms to understand the images. They produce tens of terabytes of image data every day and the experimental conditions are decided by prediction models. As new data comes in, the system generates new models, and the cycle repeats automatically and continuously.

AI is transforming the ways organizations and industries operate. However, scientific disciplines require a rigorous approach that yields accurate results and provides the transparency scientists and auditors need. Since governance, privacy, and security are not inherently baked into AI, organizations with strict requirements need to be sure that the technology they utilize is both safe and accurate.

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U.S. Dollar Collapse Primes Crypto For Trillions To Flood The SystemTriggering An Epic Bitcoin, Ethereum, XRP And Crypto Price Prediction – Forbes

October 16, 2023 | Author: admin

BitcoinBTCand the wider crypto marketincluding ethereum and XRPXRPhave lost momentum after charging into 2023 (though billionaire Warren Buffett is still quietly getting richer off bitcoin).

Subscribe now to Forbes' CryptoAsset & Blockchain Advisor and successfully navigate the bitcoin and crypto market rollercoaster ahead of next year's historical bitcoin halving!

The bitcoin price has lost 15% since peaking at almost $32,000 per bitcoin earlier this year, dragging down the price of other top ten coins ethereum and XRPdespite the market bracing for a huge Wall Street earthquake.

Now, as billionaire hedge fund manager Paul Tudor Jones issues a "cataclysmic" U.S. dollar warning, legendary bitcoin and crypto trader Arthur Hayes has predicted "trillions of dollars" are about to hit the crypto marketheralding a massive price bull run.

Bitcoin's historical halving that's expected to cause crypto price chaos is just around the corner! Sign up now for the free CryptoCodexA daily newsletter for traders, investors and the crypto-curious that will keep you ahead of the market

"Its just so ridiculous how much money is going to be printed over the next two to three years while the central banks try to save the government bond markets that I guess Im just so bullish on bitcoin, crypto, certain stocks [and] so bearish on fiat just because theres going to be umpteenth more trillions of dollars of it," Hayes, who cofounded crypto derivatives pioneer BitMex, told YouTuber Ran Neuner.

The Federal Reserve in the U.S. and central banks across Europe and Asia will, according to Hayes, be forced to issue increasing amounts of debt in coming years, boosting speculative, risk assets such as bitcoin, ethereum, XRP and other cryptocurrencies while at the same time weakening the U.S. dollar.

"We also saw some dovish shifts in Fed officials stance on monetary policy this week as a handful of them suggested higher long-term yield will do the job for them," Yuya Hasegawa, a crypto market analyst with Tokyo-based Bitbank, wrote in an emailed note, adding "the macro environment is starting to shift in favor of [bitcoin].

Last week, analysts at the investment bank Jefferies warned the Fed will be forced to restart its money printerpotentially collapsing the U.S. dollar and fueling a bitcoin price boom to rival gold.

Meanwhile, Hayes pointed to the commercialization of artificial intelligence (AI) this year as the potential catalyst for an overhaul of the economic and financial system, calling it the most transformative development of all time.

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"The [bitcoin and crypto] bull market that were about to experiencethat were on the cusp ofand its a combination of the most amount of money ever printed in human history in a two to three year period and the commercialization of AI and how that relates to crypto as the most transformative technological development thats ever happened in human history," Hayes said.

Earlier this month, Hayes generated headlines when he predicted the bitcoin price could rocket to $1 million by 2026.

"There is going to be a major financial crisis, probably as bad or worse than the great depression, sometime near the end of the decade; before we get there, were gonna have, I think, the largest bull market in stocks, real estate, crypto, art, you name it, that weve ever seen since World War 2," Hayes told YouTuber Tom Bilyeu.

I am a journalist with significant experience covering technology, finance, economics, and business around the world. As the founding editor of Verdict.co.uk I reported on how technology is changing business, political trends, and the latest culture and lifestyle. I have covered the rise of bitcoin and cryptocurrency since 2012 and have charted its emergence as a niche technology into the greatest threat to the established financial system the world has ever seen and the most important new technology since the internet itself. I have worked and written for CityAM, the Financial Times, and the New Statesman, amongst others. Follow me on Twitter @billybambrough or email me on billyATbillybambrough.com.Disclosure: I occasionally hold some small amount of bitcoin and other cryptocurrencies.

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Ferrari to accept crypto as payment for its cars in the US – Reuters

October 16, 2023 | Author: admin

The logo of Ferrari is seen in the headquarters as CEO Benedetto Vigna unveils the company's new long term strategy, in Maranello, Italy, June 15, 2022. Picture taken June 15, 2022. REUTERS/Flavio Lo Scalzo/File Photo Acquire Licensing Rights

MILAN, Oct 14 (Reuters) - Ferrari (RACE.MI) has started to accept payment in cryptocurrency for its luxury sports cars in the U.S. and will extend the scheme to Europe following requests from its wealthy customers, its marketing and commercial chief told Reuters.

The vast majority of blue-chip companies have steered clear of crypto as the volatility of bitcoin and other tokens renders them impractical for commerce. Patchy regulation and high energy usage have also prevented the spread of crypto as a means of payment.

These include electric carmaker Tesla (TSLA.O), which in 2021 began to accept payment in bitcoin, the biggest crypto coin, before CEO Elon Musk halted it because of environmental concerns.

Ferrari's Chief Marketing and Commercial Officer Enrico Galliera told that Reuters cryptocurrencies had made efforts to reduce their carbon footprint through the introduction of new software and a larger use of renewable sources.

"Our target to reach for carbon neutrality by 2030 along our whole value chain is absolutely confirmed," he said in an interview.

Ferrari said the decision came in response to requests from the market and dealers as many of its clients have invested in crypto.

"Some are young investors who have built their fortunes around cryptocurrencies," he said. "Some others are more traditional investors, who want to diversify their portfolios."

While some cryptocurrencies, such as the second-largest, ether , have improved their energy efficiency, bitcoin still attracts criticism for its energy-intensive mining.

Ferrari shipped more than 1,800 cars to its Americas region, which includes the U.S., in the first half of this year.

Galliera did not say how many cars Ferrari expected to sell through crypto. He said the company's order portfolio was strong and fully booked well into 2025, but the company wanted to test this expanding universe.

"This will help us connect to people who are not necessarily our clients but might afford a Ferrari," he said.

The Italian company, which sold 13,200 cars in 2022, with prices starting at over 200,000 euros ($211,000) and going up to 2 million euros, plans to extend the crypto scheme to Europe by the first quarter of next year and then to other regions where crypto is legally accepted.

Europe, the Middle East and Africa (EMEA) is Ferrari's largest region, accounting for 46% of its total car shipments in the first half of this year.

"Interest is the same in the U.S. and Europe, we don't see huge differences," Galliera said.

Countries where cryptocurrencies are restricted include China.

Ferrari has turned to one of the biggest cryptocurrency payment processors, BitPay, for the initial phase in the U.S., and will allow transactions in bitcoin, ether and USDC, one of the largest so-called stablecoins. Ferrari might use other payment processors in different regions.

"Prices will not change, no fees, no surcharges if you pay through cryptocurrencies," Galliera said.

Bitpay will immediately turn cryptocurrency payments into traditional currency on behalf of Ferrari's dealers, so they are protected from price swings.

"This was one of our main goals: avoiding, both our dealers and us, to directly handle cryptocurrencies and being shielded from their wide fluctuations," Galliera said.

As the payment processor, BitPay will ensure that the virtual currencies come from legitimate sources and not derived from criminal activity or to be used to launder the proceeds of crime or evade tax.

Ferrari's marketing and commercial chief said that the majority of its U.S. dealers have already signed up, or are about to agree, to the scheme

"I am confident others will join soon," Galliera said.

($1 = 0.9495 euros)

Reporting by Giulio Piovaccari in Milan; additional reporting by Tom Wilson in London; Editing by Louise Heavens

Our Standards: The Thomson Reuters Trust Principles.

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Cathie Wood’s Latest Bitcoin Insights: Is Now the Time to Buy? – The Motley Fool

October 16, 2023 | Author: admin

Ark Invest, led by the visionary Chief Executive Officer Cathie Wood, has risen to prominence in recent years due to its innovation-centric investment strategies. Its priorities on assets with disruptive potential such as DNA sequencing, artificial intelligence (AI), and blockchain technology make the firm a natural fan and notable advocate of Bitcoin(BTC 3.33%) thanks to its status as the most decentralized, secure, and valuable cryptocurrency.

To shed more light on Bitcoin, Ark publishes a comprehensive report every month delving into blockchain-based metrics, investor behavior trends, and macroeconomic factors, providing valuable context on the crypto's status and potential trajectory. In the firm's most recent report, for the month of September, several crucial insights and trends were highlighted, suggesting the path Bitcoin might take in the coming months.

Image source: Getty Images.

One of the key indicators emphasized in Ark's report is the remarkable rise in the percentage of long-term holders. Now at an all-time high, 76% of Bitcoin's total available supply hasn't moved in more than 155 days, the point at which short-term holders change to long term.

This time frame might seem arbitrary, but analytics firm Glassnode has found it to be a statistically significant benchmark as those who hold past 155 days are more likely to be smart-money investors and can provide insight into market sentiment.

Ark's analysts point out that this trend of an increasing number of long-term holders aligns with other transitional periods in Bitcoin's history, suggesting that this bear market might not be fundamentally different from previous ones.

Typically, long-term holders enter the market in droves and pick up coins on the cheap, creating an accumulation phase usually associated with post-bear markets and early stages of bull markets. Moreover, the increase in long-term holders further reduces the digital currency's already scarce supply, potentially setting the stage for a significant price surge should a bull market return.

Ark's embrace of innovation and admiration for Bitcoin extends beyond just an anecdotal report. The firm also creates unique metrics and indicators to assess its current position relative to past cycles. The latest report highlighted two specific statistics supporting Ark's stance that the cryptocurrency is currently trading at a relative discount.

The profit/loss ratio, comparing the number of coins transacted at a profit to those at a loss, currently hovers just above 1, indicating a decline in investor "exuberance" compared to earlier in the year, when Bitcoin's price jumped more than 80% in just five months. When reaching these levels in the past, the crypto was either in the depths of a bear market or clawing its way back into a bull market. Ark sees it as the latter.

Ark highlighted another crucial metric, the realized-profits-to-realized-cap ratio, which is currently at its lowest point since the beginning of the year, further indicating oversold conditions. This ratio measures profit-taking among Bitcoin investors against its realized cap, rather than the more commonly used market cap. Since realized cap is calculated by multiplying the last price at which every Bitcoin was transacted instead of valuing every coin at its current price, it offers a more sensitive and less misleading assessment than market cap.

By quantifying the amount of realized profits against Bitcoin's realized cap, Ark is able to measure buying and selling activity more clearly. Considering Bitcoin has historically entered the oversold territory of this ratio only seven times in its history, today could be an opportune moment for investors.

Ark's report doesn't shy away from acknowledging current unfavorable macroeconomic conditions and the potential challenges they pose to Bitcoin's bull market return. The prolonged interest rate hikes and tighter liquidity anticipated in markets globally could present hurdles for cryptocurrencies since typically these assets have thrived in environments with liquidity surpluses.

However, despite short-term challenges, Ark remains bullish on Bitcoin in the long run. Its analysis suggests that the cryptocurrency's resiliency and favorable developments of on-chain metrics such as the number of active owners and mining difficulty, put it in a potentially lucrative position as it navigates the fine line between bear and bull markets.

RJ Fulton has positions in Bitcoin. The Motley Fool has positions in and recommends Bitcoin. The Motley Fool has a disclosure policy.

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Cathie Wood's Latest Bitcoin Insights: Is Now the Time to Buy? - The Motley Fool

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Bitcoin nears $28,000 on US ETF approval anticipation By Investing … – Investing.com

October 16, 2023 | Author: admin

Investing.com|EditorPollock Mondal

Published Oct 16, 2023 03:37AM ET

Bitcoin's value is on the rise, nearing $28,000 as of Monday, following a 4.5% surge during Asian trading hours. The increase in the cryptocurrency's value was driven by heightened anticipation of a US Bitcoin Exchange-Traded Fund (ETF) approval, following the Securities and Exchange Commission's (SEC) decision not to appeal a court ruling regarding Grayscale's bitcoin trust.

The SEC's non-action could potentially lead to the Grayscale Bitcoin Trust (GBTC) becoming an ETF, a first in the US. This development has prompted traders to predict a further rise in Bitcoin's value.

This follows Sunday's slight increase in Bitcoin's price by 0.10% to $26,900. The cryptocurrency community was excited by the SEC's approval of Grayscale's Bitcoin ETF, announced on Friday night after a significant ruling by a three-judge panel of the Washington DC Court of Appeals. This approval marks a new chapter for the cryptocurrency sector and is expected to unlock substantial value for investors through streamlined share formation and redemption processes, preventing steep discounts to Bitcoin's inherent value.

Bitcoin's price rise is also influenced by the current low inflation environment, indicated by a 0.2% decrease in Consumer Price Index from August figures, and escalating geopolitical tensions. With a trade volume of $4.8 billion and 19,514,125 BTC tokens in circulationalmost reaching its set limit of 21 million tokensBitcoin is poised to retest the $30,000 mark, reinforcing its position as a refuge for investors against inflation and market upheaval.

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Bitcoin price: Traders tip bitcoin to hit $US100,000 – The Australian Financial Review

October 16, 2023 | Author: admin

Mr Galvin added that DACM had been fielding interest from institutional and sophisticated investors, including family offices, looking for exposure to alternative assets.

We only deal with wholesale, sophisticated investors and the vast majority are coming from overseas, he said.

Theyre probably at 1 or 2 per cent allocation [...] maybe up to 5 per cent in some cases.

At the Financial Reviews first Crypto Summit in April last year, SkyBridge Capital founder Anthony Scaramucci predicted bitcoin would hit $US500,000. At the time it was trading around $US50,000 but more than halved in the following six months.

On Monday, bitcoin was trading around $US44,000.

Self-managed superannuation funds have also piled into bitcoin and other cryptocurrencies, with the latest Australian Tax Office data showing more than $950 million is allocated to the asset class.

Both Ms Wade and Mr Galvin cited the ongoing push by global institutions to establish exchange-traded cryptocurrency funds as a potential catalyst for inflows to the industry.

Several traditional wealth management giants, including BlackRock, Fidelity, Ark Invest, VanEck, JPMorgan and BetaShares, have all signalled to US regulators their desire to operate ETFs that track bitcoins price. The decision remains with US regulators.

Vimal Gor, who left fund manager Pendal in 2022 to join alternative fund manager Trovio, said when a bitcoin EFT was launched he expected institutional adoption to happen quickly.

Institutional adoption begets more institutional adoption. If you go back to pre-the Blackrock ETF filing, it was debatable whether it was even considered an asset class [...] theres no question whether its an asset class now, he said.

Im a strong believer that institutional adoption and retail adoption will happen a lot quicker when an ETF comes and I think that will turbocharge the whole space.

Speaking at an earlier session, Jeff Yew, the chief executive of crypto asset manager Monochrome, also noted the big names backing bitcoin ETFs, which would hold actual cryptocurrencies for investors, as a sign of institutional interest.

That shifted the entire taboo of talking about bitcoin in professional finance. The career risk of talking about bitcoin, or having that sort of conversation is now almost fully removed in the US, he said.

Jeff Yew, founder and CEO of Monochrome, is trying to get two new crypto ETFs approved in Australia.Michael Quelch

I think the ETF will unlock a lot of institutional wave investment into crypto that wouldnt otherwise touch it outside the regulatory structure.

Mr Yew, former chief executive of cryptocurrency exchange Binance Australia, has been trying to get two new crypto ETFs approved in Australia, lodging his intention with the Australian Securities and Investments Commission and the ASX.

While Mr Yew did not disclose if or when Monochromes proposed ETF would be likely to hit the market, he did say he was watching US developments. Mr Yew pointed to January 10, which marks final deadline for the US SEC to review ARKs ETF application, as a date he was watching closely.

January 10 is an update that a lot of people in this industry are laser-focused on. Were doing the same thing as well.

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Bitcoin signals potential range expansion Will SOL, LDO, ICP and … – Cointelegraph

October 16, 2023 | Author: admin

The S&P 500 Index nudged higher by 0.45% to record its second positive week. While the United States equities markets were a slow mover, gold witnessed a massive run-up of more than 5% this week. Its rally of 3.11% on Oct. 13 was its best one-day performance since Dec. 1 of last year. However, the Bitcoin (BTC) bulls did not have any such luck as Bitcoin is on track to end the week down more than 3%.

Bitcoins weakness and the regulatory overhang have kept crypto investors away from altcoins. That has kept Bitcoins market dominance hovering near the 50% mark for the past few days.

Market observers are likely to keep their focus on Bitcoin for the next few days. The longer the bulls sustain the price above $25,000, the greater the possibility that the next move is likely to be higher. A bullish move in Bitcoin is likely to spur buying in select altcoins as crypto investors will then sense a bull market.

Select cryptocurrencies are showing signs of forming a base. If they breakout to the upside, a new up-move may start. Lets study the charts of the top-5 cryptocurrencies that could outperform in the near term.

Bitcoin has been trading between the moving averages for the past few days, indicating indecision between the bulls and the bears about the next directional move.

Usually, a tight consolidation is followed by a range expansion. In this case, if buyers kick the price above the 20-day exponential moving average ($27,110), the BTC/USDT pair could rise to $28,143. The bears are expected to mount a strong defense at this level.

Alternatively, if the price turns down and dives below the 50-day simple moving average ($26,671), it will signal that bears have asserted their supremacy. The pair may first drop to $25,990 and thereafter to the pivotal support at $24,800. This level is likely to attract aggressive buying by the bulls.

The pairs recovery is facing selling at the 20-EMA on the 4-hour chart but a positive sign is that the bulls have not given up much ground. This suggests that the buyers are not rushing to the exit and are keeping up the pressure.

If the 20-EMA is taken out, the pair could first rise to the 50-SMA. This level may act as a minor barrier but if overcome, the pair could climb to $27,750 and then to $28,143.

On the contrary, if the bulls fail to pierce the 20-EMA, the sellers will sense an opportunity to pull the price lower. A dump below $26,500 could sink the pair to $26,000 and then to $24,800.

Solana (SOL) has been witnessing a tough battle between the bulls and the bears near the 20-day EMA ($21.77). This suggests that the bulls are trying to flip this level into support.

There is a minor resistance at $22.50 but if this level is crossed, the SOL/USDT pair could rise to the neckline of the inverse head and shoulders pattern. A break and close above this resistance will complete the bullish setup. Buyers may face a stiff resistance at $27.12 but if this hurdle is cleared, the pair could surge to the target objective at $32.81.

This positive view will be negated in the near term if the price turns down and plunges below the 50-day SMA ($20.50). That could start a descent toward $18.58 and then to $15.33.

After trading between the moving averages for some time, the price resolved to the downside with a break below the 20-EMA. This indicates that the bears may remain in control. The pair could first fall to $20.93 and if this level also cracks, the pair may collapse to $20.

Conversely, if the price fails to sustain below the 20-EMA, it will suggest solid buying at lower levels. The first sign of strength will be a break and close above the 50-SMA. That could open the doors for a rally to $23.50 and then to the neckline of the inverse H&S pattern.

Lido DAO (LDO) has been trading near the moving averages for the past few days, indicating that the bears may be losing their grip.

The moving averages have flattened out and the RSI has jumped into the positive territory, indicating that the bulls are attempting a comeback. The immediate resistance on the upside is $1.73. If this level is scaled, the LDO/USDT pair could climb to the downtrend line. This level is again likely to witness a tough battle between the bulls and the bears.

Contrarily, if the price turns down and skids below the moving averages, it will suggest that the bears are in command and are selling on every minor rally. The pair may then retest the vital support at $1.38.

The 20-EMA has started to turn up on the 4-hour chart and the RSI is in the positive area, indicating that bulls have the upper hand. There is a minor resistance at $1.63 but it is likely to be crossed. The pair could then rise to $1.73.

If bears want to weaken the bullish momentum, they will have to quickly drag the price back below the moving averages. The pair could then slump to the $1.45 to $1.50 support zone.

Related: Bitcoin traders eye weekly close volatility with $27K BTC price on radar

Internet Computer (ICP) has been consolidating in a tight range between $2.86 and $3.35 for the past several days.

The RSI has formed a positive divergence, indicating that the selling pressure is reducing. The ICP/USDT pair could next reach the overhead resistance at $3.35. A break and close above this level will signal a potential trend change. The first target on the upside is $4 and then $4.50.

Contrary to this assumption, if the price turns down from $3.35, it will suggest that the pair may extend its stay inside the range for some more time. A slide below $2.86 will indicate the resumption of the downtrend.

The moving averages have completed a bullish crossover and the RSI is in the overbought zone on the 4-hour chart. This indicates that the buyers have the upper hand. The pair is likely to reach the overhead resistance at $3.35 where the bears may to pose a strong challenge.

If the price turns down from $3.35, the consolidation may continue for a while longer. On the other hand, if buyers kick the price above $3.35, it will indicate that the bulls are in charge. The pair may then soar to $3.74 and later to the pattern target of $3.84.

VeChain (VET) has been trading inside a descending triangle for the past few days. Although this is a negative pattern, the price has been clinging to the downtrend line for the past few days, which is a positive sign.

The moving averages have flattened out and the RSI is near the midpoint, indicating that the bearish pressure may be reducing. Buyers will try to propel the price above the downtrend line. If they succeed, it will invalidate the negative setup. That could start a new up-move toward $0.021.

Instead, if the price turns down from the current level, it will suggest that bears continue to defend the downtrend line with vigor. The bears will then again try to pull the price to the critical support at $0.014.

The 4-hour chart shows that the price has been trading inside the falling wedge pattern. Buyers are trying to push and sustain the price above the 50-SMA. If they do that, the VET/USDT pair could reach the downtrend line of the wedge. A break and close above the wedge could start a new up-move.

The bears are unlikely to give up easily. They will aggressively defend the zone between the 50-SMA and the downtrend line. If the price turns down sharply and slides below the 20-EMA, it will indicate that the pair may remain inside the wedge for some more time.

This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.

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Bitcoin signals potential range expansion Will SOL, LDO, ICP and ... - Cointelegraph

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