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The finance world is changing big time, driven by a wave of innovative technologies collectively known as Fintech. But what exactly is it? In a few words, it is a dynamic domain where IT companies like Relevant Software are developing tools and solutions that are transforming the way we manage our money. 

Why is this transformation so critical? Traditional financial services, while established, are often riddled with inefficiencies, limited accessibility, and a lack of personalization. This translates to a frustrating and time-consuming experience for customers, who increasingly demand agility, convenience, and a tailored approach to their finances. 

So, how can Fintech address these challenges? Let's look at the details.

Digital Banking

Fintech innovations are breaking down barriers to financial inclusion. Millions of people worldwide still lack access to basic financial services. Fintech is bridging this gap with mobile-based solutions that don't require traditional bank accounts. This allows individuals to save, send, and receive money securely, promoting financial independence and inclusion. 

Payment Innovations

Remember when making a payment meant writing a check or waiting days for a bank transfer to clear? Those days are long gone. Now, peer-to-peer payment apps, contactless payments, and instant payment systems are the norms, radically reducing transaction times and increasing user convenience. 

Automation and AI

Fintech introduces automation solutions powered by Artificial Intelligence (AI) that streamline tedious manual tasks. Mortgage approvals, for instance, can be significantly expedited with AI-driven document processing and risk assessment, saving both time and resources for lenders and borrowers. Similarly, AI-driven chatbots can handle customer inquiries 24/7, providing a level of service that was unimaginable just a few years ago. 

Low Code Platforms

Low code platforms are shining as a new trend in fintech innovation. By using visual tools instead of writing code, creating fintech apps becomes much easier, helping close the skills gap. Fintech newcomers can harness the power of low-code platforms to quickly bring to life innovative ideas that stay in step with market trends

Blockchain and Cryptocurrency

It's impossible to talk about Fintech without mentioning blockchain. Through this technology, one can perform transactions securely and with transparency, without reliance on a centralized authority. Additionally, blockchain is used to prevent fraud, streamline cross-border payments, and improve supply chain transparency.

RegTech

The fintech sector moves fast, often outpacing regulatory frameworks. This can lead to a gray area where innovations flourish without adequate oversight, potentially leading to risks for consumers and the financial system at large. Therefore, collaboration between fintech companies, traditional financial institutions, and regulatory bodies is crucial to ensure that innovations benefit everyone without compromising security or fairness. 

InsurTech

Insurance is another area ripe for disruption. InsurTech companies are utilizing tech to make insurance options more economical, widely available, and tailored to specific preferences.. Think pay-as-you-drive car insurance, or parametric insurance that pays out based on specific events, like a natural disaster.

Open Source & SaaS

For fintech startups, being quick and adaptable is key. That's where open source and SaaS (Software as a Service) come in. They allow companies to use and improve software without the hassle of managing it. This means more time focused on customers and less on tech headaches. 

Embedded Finance

This means users can access financial services through non-financial platforms. Think buying insurance from your favorite online store or getting a loan from your ride-sharing app. It's making finance a seamless part of everyday life. 

It's easy to get caught up in the excitement of all these innovations, but it's also essential to approach them with a critical eye. Regulatory hurdles, security concerns, and the digital divide (the gap between those with access to digital technologies and those without) are just a few of the issues that need addressing. Moreover, as the financial sector increasingly relies on technology, the risk of cyberattacks constantly grows, necessitating robust cybersecurity measures. But the potential benefits—increased accessibility, efficiency, and personalization of financial services—are too significant to ignore. 

And what about the traditional banks? Some may argue that fintech is spelling doom for conventional banking institutions, but that's not entirely accurate. Sure, fintech is disrupting the status quo, but it's also pushing banks to innovate and adapt, leading to collaborations that combine the best of both worlds. Traditional banks are leveraging fintech to enhance their digital offerings, making banking more accessible, efficient, and customer-friendly. 

Therefore, what can we expect for financial services moving forward with the rise of Fintech? It's a question many in the industry are pondering. While the trajectory seems clear—more automation, increased personalization, and further democratizing financial services—the pace and nature of these changes remain fluid. 

What's certain is that those who can adapt to and leverage these innovations will find themselves at the forefront of a new era in finance. The journey is complex, but the destination—a more inclusive, efficient, and secure financial ecosystem—is undoubtedly worth the effort.

 

The fast development of technology, however, has made it evident that the metaverse is no longer a far-fetched prospect. It's important to think about how other technologies, like GPT (Generative Pre-trained Transformer), may be used to improve and mold the metaverse experience as it takes shape.

Options For Using GPT In The Virtual World

OpenAI's GPT is a cutting-edge language processing AI that can create natural-sounding text for several uses. It's not clear what all the possible applications of GPT in the metaverse might be.

Machines That Act As Though They Are Human

The development of chatbots and other forms of artificial intelligence is one area where GPT might be put to use in the metaverse. These AI-driven creatures might provide useful guidance to users as they explore the metaverse in search of particular information or engage in social activities. Metaverse information, including descriptions of virtual environments or character dialogue, might similarly be generated using GPT.

Engaging Interactions

In the metaverse, GPT may also be used to design engaging activities. To create more interactive and unique experiences, GPT might be used to generate replies to user input. Using GPT to create personalized replies and difficulties for each user might be very helpful in online events and games.

Possible Advantages Of GPT In Promoting Metaverse Usage

Not only may GPT be useful in the metaverse, but it also possesses the ability to increase support for the metaverse as a whole.

Making this virtual setting available to a larger audience might be a positive outcome. The ability to employ GPT to create material and text in many languages would greatly expand the potential audience for the metaverse.

Virtual Reality Game Theory

The metaverse is shorthand for a completely engaging and realistic virtual reality. It's a digital world where users may experience lifelike interactions with other users, virtual items, and surroundings. The metaverse has been hailed by some as a game-changer for society because it can facilitate new forms of interaction and innovation in the workplace and the daily lives of people everywhere.

Digital Personas

Potential applications of GPT in the Metaverse include the development of more lifelike and convincing digital avatars. Given GPT's capacity to produce text that sounds human, it may be used to program artificial personalities capable of holding convincingly realistic conversations with people. Using this, we could build digital helpers, digital friends, and digital educators and trainers.

Experiences That Are Unique And In-Depth For Each Individual

The development of interactive and sensory encounters is yet another possible use of GPT in the metaverse. It is feasible to develop a system that generates material specific to the patient's likes and choices by developing a GPT algorithm on data associated with a certain VR machine or activity. Individually tailored quests, adventures, and tests might be made possible using this information.

Several Metaverse Advantages of GPT

Utilizing GPT in the metaverse may provide several advantages. Notable examples include the following:

Better realism and believability in virtual characters and environments means more interactive and immersive experiences in the metaverse built with GPT. This has the potential to increase the number of people using the metaverse by making it more enticing to them.

Customization: GPT may make material according to the individual's tastes and preferences. The result would be a more customized and individual encounter in the metaverse, which might be more enticing to certain people.

Advantageous for those without the proper tools to fully engage in a more complicated virtual environment, GPT may be used to construct virtual assistants and other interactive characters that can help users. 

What's The Deal With ChatGPT And Other AI Chatbots?

Using large neural network models, advanced chatbots can grasp human language thanks to advancements in the natural-language processing software. Thanks to learning algorithms, they're capable of comprehending a broad spectrum of human languages and offering a diverse range of responses to client queries.

Data retrieval, customer support, and even creative writing are just some of the domains where this innovation might be put to use. The adaptable software lets you modify the bot's vocabulary and dialogue in response to customer feedback. ChatGPT's platform is extensible, thus it can be utilized to develop several separate chatbots simultaneously.

Possibility of Metaverse Enhancement and Revolution via GPT

Taking both GPT and the metaverse together, we might see a dramatic shift in how we perceive and navigate virtual worlds. Using GPT to design smart entities that can comprehend and react to human speech allows us to build more lifelike virtual worlds. If this happens, more people will want to visit the metaverse, and companies will have more chances to utilize it as a marketing and sales tool.

In addition, GPT's deployment in the metaverse has the potential to inspire the creation of novel uses, such as e-learning, treatment, and travel in virtual worlds. Future innovations are certain to be much more intriguing as GPT as well as the metaverse begin to mature.

Is the Metaverse Finally Over?

Based on everything we've heard so far this year, it seems like AI chatbots would be extremely appreciated in the Metaverse. Providing consumers with an environment that is more lived-in and natural than what is currently available on other platforms.

DappRadar data shows that across the two metaverse sites, Decentraland as well as the Sandbox, there were fewer than 1,000 "everyday regular members." The figures above only account for the overall amount of purse addresses that have interacted with the system's consensus mechanism. Not total visitors, which are larger, however still under 10,000.

Could Artificially Intelligent Chatbots Fill in the Blanks?

This author has shown that AI chatbots stand out too much in a heavily packed metaverse to be considered ambient noise. After all, today's chatbots are disciplined enough that they won't engage in trolling or flaming; they're more likely to be model citizens of the internet. Humans, in general, are not this way. It's not terrible, but it may render the metaverse a little blander.

A future without technology that could recreate an infinite number of human-like talks, however, is unthinkable. It's just too simple and inexpensive to be worth doing.

What's The Deal With ChatGPT And Other AI Chatbots?

Using deep neural network models, modern chatbots can grasp human language thanks to advancements in natural language processing technology. Thanks to machine learning, they're capable of comprehending a broad variety of human languages and providing a wide range of responses to user queries.

Information retrieval, customer service, and even creative writing are just some of the domains where this technology might be put to use. The adaptable software lets you modify the bot's vocabulary and dialogue in response to user input. ChatGPT's technology is extensible, thus it may be used to develop several separate chatbots simultaneously. In most cases, a chatbot powered by AI will have three main parts.

Three models are utilized to produce replies according to user factors of production: a natural language comprehension engine, which understands natural speech utilizing machine learning utilizing billions of instances throughout the web; a language processing model, which produces natural speech and develops tailored answers; as well as a conversation regulation network.

All artificially intelligent chatbots have these three components, which are built upon one another. Collectively, they contribute to a simulation that is almost human in its realism.

In-Metaverse Robotic Customer Support

Companies have flocked to metaverse networks as their popularity has increased to meet user demands and raise product awareness. Some of the most well-known companies in the world have opened up shop there, hoping to get customers to spend their money once again. Over the last two years, several well-known brands, including Calvin Klein, and even KFC, have opened virtual storefronts in the virtual world of Decentraland.

Chatbots powered by AI allow big conglomerates to staff their shops without increasing their wage expenditure. Since the middle of the 2000s, chatbots providing customer support have become more widespread. Using MSN Messenger, SmarterChild had preteens and teenagers chatting with a computer as soon as 2001.

While previous AI generations were impressive, the next generation is light years ahead of anything seen in the 2000s. Non-crypto natives may benefit from this human-like customer support as they learn to navigate the Metaverse. Although the crypto industry is well aware of automated tools like Bitcoin 360 Ai, you can learn more here. While it would be helpful to have a buddy who is well-versed in Web3 and DeFi protocols, not everyone has access to such a person.

Reading through several internet discussion threads and how-to articles helps shorten the learning curve. However, sophisticated AI chatbots may eliminate the need for specialized vocabulary, making decentralized finance and NFTs accessible to a far wider audience.

Things Aren't Perfect

Nevertheless, there are drawbacks to this. The prevalence of low-cost, almost limitless chatbots as mobile, speaking characters runs the risk of watering down the Metaverse's credibility. If you have had any kind of extended experience with a contemporary chatbot, you know they aren't quite right. They have excellent replies, but they tend to follow a pattern.

A recent analysis by Barracuda claims that bots now account for 64 percent of all website traffic. Even while most robots are fundamentally distinct from AI-powered characters, it's not out of the question that AI-powered characters may come to dominate the Metaverse.

If you bombard a popular AI chatbot with questions, eventually it will give you an incorrect answer. Moreover, they often express their falsehoods with stern assurance. After barely three days online, Meta, Zuckerberg's AI company, shut down Galactica, an artificial intelligence language model educated on scholarly papers, because of erroneous and biased findings.

The use of artificial intelligence chatbots like ChatGPT may potentially increase the invasiveness of capitalist monitoring systems. If sometime in the not-too-distant future we devote loads of effort to metaverse portals, a world inhabited by AI chatbots is merely the next step in the Big Data business model.

A certain amount of moderation on the side of both systems and consumers is going to be necessary when it comes to the usage of fabricated human avatars. Although advantages now outnumber disadvantages, this might change in the future. Since, in a dystopian future, it may be impossible to tell human beings from non-human ones, this is an important question to ask. Hopefully, the fact that this essay doesn't sound like it was typed by a robot is the only clue you need to know that a human being was behind its creation. This difference will vanish in the foreseeable future.

Final Thoughts

There are no constraints whatsoever. Increasing numbers of people will use ChatGPT to make stunning text-to-image suggestions as its popularity grows. Engineers will likely compete with one another to create the most difficult and creative suggestions.

One fact is certain, though: text-to-image exercises are a fantastic method to hone your writing abilities and push your imagination.

 

However, it’s important to understand that AI is more of a trader’s sidekick than their replacement. The factors that impact asset prices are complex and non-linear, which makes DNNs the best option for detecting these relationships. Then, the AI can make informed decisions about when and where to trade. Visit the official site of Quantum AI Trading to get more information. 

Predictive Models

While there is an opportunity for pure-play AI trading success, building a strategy that outperforms the market takes more than just a clever algorithm. Traders need to factor in transaction fees, slippage, and the fact that markets change constantly. All these things add up and can cancel out any profits that an algorithm might make in a simulation.

AI algorithms can identify patterns in data that may go unnoticed by humans, which is particularly beneficial in high-frequency trading. They can also process data much faster than humans, which helps recognize ephemeral trading opportunities.

Market Sentiment Analysis

Sentiment analysis is a valuable tool for companies to identify and capitalize on market opportunities. It examines the perceptions and attitudes of consumers through online platforms like social media. Sentiment analysis can help businesses understand their customers’ needs and expectations and determine their preferred choices. It can also identify trends in customer sentiment and anticipate changes in consumer behaviour.

Many machine learning algorithms use data mining to make predictions based on patterns and correlations in complex data sets. This can improve the accuracy of predictive models and enable traders to take advantage of new trading opportunities. 

A growing number of financial institutions are deploying machine learning technologies to detect and capitalize on market opportunities. They can also automate processes and reduce human intervention, enabling them to make thousands of trades per day.

Risk Management

In a trading environment that is increasingly complex and nuanced, factors that impact asset prices do not always have straightforward linear relationships. DNNs, which rely on layers to process data hierarchically, can discern such relationships that may go unnoticed by traditional models.

Moreover, ML-powered algorithms can reduce the time and resources needed to assess risk factors in large data sets. However, these tools are prone to errors related to bias and variance. These risks require attention and mitigation.

Traders must be aware of the limitations and risks associated with AI for trading. For instance, the accuracy of input data and the availability of reliable data are crucial. They must also be mindful of ethical and regulatory considerations, which are evolving rapidly. These factors can influence how AI and ML are deployed in the financial sector and their impacts on its performance and stability. Ongoing research is necessary to better understand the evolving adoption of these technologies and address any emerging issues.

Trading Strategies

Traders must scour a huge amount of data to find information that will increase their profit margin. AI algorithms can help them do that by analyzing market information and identifying patterns that humans may not be able to detect.

It is important to note that despite all the hype about the role of AI in trading, it will not replace human traders any time soon. Instead, it will be a trader’s sidekick, improving their ability to spot opportunities and make smarter trading decisions. 

 

AI software can read market trends and patterns faster than humans and provide you with a more in-depth analysis of the market.

Many platforms offer a fully automated service, where the AI will trade on your behalf. You can also find a range of third-party AI bots in the MQL5 marketplace. Check the official site of the company that is providing the best-automated service. 

Trade Ideas

Trade Ideas is a comprehensive AI trading tool that provides advanced stock scanning, charting, and automated trading services. It is compatible with most brokers and offers a variety of high-quality data sources. It also features Holly Artificial Intelligence, which helps traders identify unique trading opportunities. It is ideal for advanced traders who are familiar with financial markets and trading strategies.

The platform’s powerful AI-powered market scanner, Holly AI, automatically analyzes past market action to find potential trading opportunities that meet a trader’s risk and investment objectives. It can also track a user’s portfolio performance and identify risks.

Its unique Compare Count Window allows users to visually compare strategies side-by-side and can be used for both long-term and short-term trading. It is a valuable feature that makes Trade Ideas a standout among other market scans. It also offers a wealth of support resources, including training classes and a trading education library. This demonstrates their commitment to growing a knowledgeable trading community.

MT5

MT5 is an evolution of the popular MT4 platform, which offers advanced trading tools for forex, commodities and index instruments. The platform is highly adaptable and can be accessed from all types of devices including mobile phones, tablets and laptops.

The MT5 platform is designed to facilitate algorithmic trading with a robust suite of automation and EA tools, which can be purchased from the MT5 Market. The platform also allows traders to create their trading programs using the custom programming language MQL5 and a built-in Policy Editor.

The platform provides an extensive range of analytical tools, including 38 technical indicators, 21 chart timeframes and 44 graphical objects that can be used to identify trends and patterns. Traders can also access a wide variety of news feeds and economic calendars through the MT5 terminal. MT5 is a 64-bit, multithreaded platform, which means that programmed strategies can run faster than on MT4. This makes MT5 a better choice for those looking to execute complex trading algorithms.

Trading Technologies

There are a few things to look out for when choosing an AI trading platform. First, it’s important to consider the type of trades the platform offers. For example, some offer copy trading, which allows you to mirror the investments of an experienced trader. This is a good option for those who aren’t comfortable with allowing an AI to trade on their behalf.

Another thing to look out for is an AI that offers real-time signals for digital assets. This can help traders make informed financial decisions based on their goals and appetite for risk.

Finally, you should check that the AI trading platform offers backtesting capabilities. This involves testing the trading rules against historical market data and assessing their viability. This can save you from making costly mistakes and ensure that your AI trading strategy works as intended.

 

But it is also embracing some of its greatest opportunities, enabled by data ubiquity and high-speed processing. You can visit the official site of Ethereum Code to learn more about artificial intelligence's significant role in financial markets. 

AI can process large volumes of structured and unstructured data much faster than humans can, thereby helping traders make better decisions.

Analyze data and Make Decisions Faster

In a market where profit opportunities are ephemeral, it’s important to have a trading strategy that can react quickly to change. As a result, many traders now use AI algorithms to analyze data and make decisions faster than humans can. This technology can also help reduce the amount of time that traders spend on administrative tasks such as calculating trade costs and identifying market trends.

Using natural language processing, AI systems can also analyze textual data and extract valuable insights from news articles and social media posts. This technology can help identify sentiments and emotions, which may be overlooked by human analysts.

The integration of AI into financial markets has had both positive and negative consequences depending on how it is used. While it can improve the accuracy of predictions and reduce risk management costs, it also poses new challenges in terms of transparency and accountability. Consequently, it is important to consider the impact of AI before implementing it into trading strategies.

Effective Way to Increase Profits

Big data analytics is transforming many industries and financial markets are no exception. Currently, the world creates 2.5 quintillion bytes of data every day and this huge amount of information can be leveraged in a variety of ways to increase profitability.

AI algorithms can help to analyze large amounts of data and identify patterns that can improve business performance. For example, using natural language processing to read and understand news articles can enable a faster and more efficient research process for investment opportunities. Another use is in high-frequency trading, where AI algorithms can recognize trends and patterns more quickly than humans and therefore make trades more efficiently.

Respondents to this survey indicated that ML and AI are becoming essential facets of contemporary finance, aiding in refining decision-making and optimizing resource distribution (Table 2). Algorithmic trading and risk management surfaced as primary areas for ML and AI applications, reflecting the growing trend of integrating cutting-edge technology into financial markets.

Forecast Future Events and Trends

Predictive models are a subset of data analytics that forecast future events, anomalies, trends, and patterns using historical and current data. These models are often created through statistical algorithms, and some of the most popular include linear regression, logistic regression, decision trees, and neural networks.

These models can be used to predict anything from weather patterns and consumer sentiment shifts to credit risks and corporate earnings. They can help businesses identify opportunities for growth and make better decisions about what they should do next.

These predictive models are becoming increasingly useful for financial markets because of their ability to detect nonlinear characteristics and other complex relationships that humans cannot grasp easily. They can also be applied to large datasets, making it easier for companies to track customer behaviour and predict trends. This can help businesses create personalized products and services for their customers and increase profits. The models can work fast, too, so that business owners can get results in real time.

 

Artificial Intelligence (AI) is the latest disruptor that has taken the trading world by storm. Whether you're a seasoned trader or a novice just stepping into the trading arena, harnessing the power of AI can be a game-changer. Get the experts' guidelines by visiting the official site of Immediate Connect and learn to improve your trading strategy with AI.

Data is the Fuel for AI

To improve your trading strategy with AI, you must first appreciate the pivotal role of data. AI models require high-quality data for accurate predictions. Market data, financial reports, news feeds, and even social media sentiment can all be valuable sources. 

Collecting and preparing data for AI analysis is a critical step that often requires collaboration with data scientists. The old adage, "garbage in, garbage out," holds true; clean, reliable data is essential for effective AI-based trading strategies.

Selecting the Right AI Tools

Choosing the right AI tools for trading can be a daunting task. There's a wide array of AI-powered software and platforms available, each with its unique features. It's crucial to match your chosen tools with your trading objectives and risk tolerance. Some popular AI tools for trading include algorithmic trading platforms, sentiment analysis software, and robo-advisors. 

Algorithmic trading platforms execute trading strategies automatically based on predefined criteria, while sentiment analysis tools gauge market sentiment from news and social media. Robo-advisors provide automated portfolio management.

It's important to conduct thorough research and, if possible, consult with experts to find the best AI tools that align with your trading style. Remember that these tools should enhance your decision-making process, not replace it entirely.

Developing AI-Based Trading Strategies

Now, let's discuss the core of the matter – crafting AI-based trading strategies. There are several approaches, but the following are fundamental steps to develop effective strategies:

AI is a powerful tool that can significantly improve your trading strategy, provided you understand its capabilities and limitations. By focusing on data quality, selecting the right AI tools, and diligently developing and monitoring your AI-based strategies, you can harness the potential of AI to enhance your trading performance. 

Keep in mind that AI is not a magic bullet, but when used wisely, it can be a game-changing asset in your trading arsenal. Continuously learn, adapt, and combine human expertise with AI insights to navigate the complex world of trading successfully.

 

Last year, it began applying a similar approach to tech investments, launching an “Innovation Fund” that enables individuals to access venture capital-type investments in tech companies before they go public. Now just over a year old, the fund is focusing on investments in the data infrastructure, artificial intelligence, and property technology sectors. 

“We created something new, which is a venture fund that the public can invest in,” said Fundrise CEO Ben Miller on the “Financial Samurai” podcast with Sam Dogen. 

Investors can buy into the fund for as little as $10, a far cry from the usual six-figure minimum requirements to buy into traditional VC funds. It currently has a total of 19 assets ranging from early--, mid-, and late-stage private companies to a few public companies.

“We launched the Innovation Fund to democratize investing in these companies because they're not public. OpenAI is not a public company. Databricks was not a public company. Canva is not a public company. I think that everybody needs to be able to invest in these companies,” said Miller on his company’s “Onward” podcast

“And then, just to get a little bit into the weeds, we are not taking a 20% carried interest. We're not charging this very massive toll, 20% toll to enable it. I think that’s one of the reasons why we started our tech fund. That’s a practical thing I’m trying to do.”

Fundrise on AI: ‘Our Job Is To Get in the Middle of It’

Miller told Dogen that he’s extremely optimistic about the future of AI and that Fundrise’s Innovation Fund will invest accordingly. He compared the current boom in generative AI to the advent of the internet in terms of creating value, noting that a recent Goldman Sachs study projected that AI could double gross domestic product growth and account for 500 times the productivity gains that resulted from the invention of the personal computer. 

“So the amount of value created and captured here is going to be astronomical,” Miller said. “And that has nothing to do with us. We just happened on the scene when that’s happening. And our job is just to get in the middle of it as much as possible because that’s what’s happening today and that’s the opportunity. It’s unbelievable.”

While there are still relatively few private companies developing large language models — the foundation of generative AI applications — Miller sees an opportunity to invest in companies that provide the necessary data infrastructure for this new technology to thrive. 

“We can play AI at different places in the stack. The data infrastructure is sort of the platform level underneath AI,” he explained.

“You could also think of it as if there’s a gold rush, you can try to find gold or you can sell picks and shovels. The data infrastructures are the picks and shovels. Everybody needs these technologies to be able to do the stuff that is the application. And we’ve been investing like crazy into the picks and shovels because that’s clear and yes, and pricing the [large language models].” 

The fund is also backing companies that stand to benefit from improvements in AI technology. For example, in September, it invested $6.2 million in Canva, an online design and visual communication platform that enables users to create social media-friendly images and text using LLMs. 

Data Infrastructure Investments

The Innovation Fund’s largest position is its investment in Databricks, a data infrastructure provider with a valuation of $43 billion. Fundrise has invested $25 million, a quarter of the Innovation Fund’s holdings, into the company. 

Databricks’ software is used by over 10,000 organizations worldwide. It has raised roughly $500 million from investors such as Andreessen Horowitz, Baillie Gifford, ClearBridge Investments, and NVIDIA. It recently crossed a $1.5 billion revenue run rate at over 50% revenue year-over-year growth and acquired MosiacML, a leading generative AI platform. 

“They are one of the great companies in the world right now. Everybody who's in the tech space knows Snowflake's been absolutely on a tear. Databricks is comparable to Snowflake in terms of opportunity and excellence, in my opinion, many ways better,” Miller told Dogen. 

“To be able to get Databricks now and own a chunk of that company is just so exciting and they are integral. It's a different risk profile. You're not taking a ‘Is this company going to be successful?’ risk. You're taking a ‘how much are they going to grow?’ risk. And I think they're going to grow a lot.” 

The Innovation Fund has also made smaller investments in other data infrastructure companies, including Immuta and Vanta. 

Proptech Expertise

As a company that started in the real estate space, Fundrise has an inside perspective on the proptech sector, and its Innovation Fund has targeted several companies in the space that it uses to help manage its properties. 

Its first proptech investment was in the property inspection software platform Inspectify. The fund invested $4 million in the early-stage company, which is currently valued at $47 million. 

The fund followed this up with a $2 million investment in Jetty, a mid-stage company that provides a unified financial services platform for renters and property owners with four features: security deposit replacement, renter's insurance, flexible rent payments, and rent reporting. 

Miller noted that these investments in technologies that are adjacent to its platform mean that the fund can add value to these companies. 

“When we sent out our email to our investors saying, ‘Hey, we invested in Inspectify,’ Inspectify’s web traffic doubled," he told Dogen. “They got a hundred sales leads, which in B2B business is a lot. If we can get more investors, we’re more valuable to the companies we invest in. And so our long-term play is that we bring something to the table that's different. Sequoia brings all sorts of things to the table. They don’t bring 2 million investors. So there's a potential network effect.” 

‘You Have To Get Access’

The Innovation Fund is still in its early stages, and it’s too early to assess how this unique approach to venture investing will play out. It’s clear, however, that the fund is doing something different than the standard VC and providing unprecedented access to retail investors who don’t necessarily have thousands of dollars to invest.

“If you have money you say, ‘I want to buy Nvidia, Google,’ you can do that, but if you want to buy the best tech companies in the private markets, you can’t,” said Miller. “You have to get access.”

By Alexandra Mousavizadeh, CEO and co-founder of Evident

 

The rush to deploy Generative AI tools like ChatGPT has created a backlash and led to calls for a pause on deployment while we work out how to regulate these powerful systems. The challenge is one of imagination - what should the regulation look like and how should it be enforced? If they have the will to lead, the banks might hold the key to a workable solution...

 

The basis for The Future of Life Institute’s call to pause experimentation with large artificial intelligence (AI) systems was to buy some time. Time to do what, exactly?

 

OpenAI’s CEO and founder, Sam Altman, has argued that a vital ingredient for a positive AI future is an effective global regulatory framework. Yet no one can agree what this might look like. The 18,980 signatories to the open letter, (some of whom have since backed out or claimed to have been misrepresented) have not put forward a plan.

 

The current regulatory landscape for AI is a messy patchwork of national- and industry-level initiatives. These range from FTC and FDA efforts to address specific, yet limited industry use cases, to the EU’s AI Act and the US’s Algorithmic Accountability Act - both admirable in their intent to create a more universal framework, but flawed in their appraisal of risk.

 

Crucially, there has been no consensus reached amongst technologists, executives or regulators regarding what it’s like to be an end-user of AI-based products, and hence, what sort of regulatory framework is appropriate to pursue.

 

Like opening a bank account

For many people, AI and its potential harms remain theoretical or fantastical - conjuring up images of Terminator and Skynet rather than practical concerns. And yet, seen within an industry-specific setting such as financial services, it’s easier to understand the AI risks that are already emerging. For example, being defrauded of your life's savings, unfairly denied insurance for medical care, or extorted over loan repayments.

 

I’d argue that being an end user of an AI system is certainly comparable to a customer opening a new bank account, stepping onto a plane or taking a prescription pill - all industries that require strict external oversight due to the acknowledged risks involved.

 

When we open a bank account, we do so with the knowledge that we are protected by a rigorous, dutiful and democratically constructed set of regulation-enforced and accredited safety standards which are subject to external oversight. The regulator sets the standards for the industry, and while it won’t fully prevent bank runs, ID fraud or other depositor woes, it protects the vast majority of customers most of the time - to the benefit of the industry, and society at large.

 

It follows that we ought to create similar standards for any providers seeking to offer AI-based products within these industries and ensure clear oversight to prevent any breaches - intentional or otherwise - from occurring. We should even consider setting the bar higher when it comes to AI standards, due to the potential speed, scale and scope of deployment that ChatGPT has shown to be possible for these systems.

 

Banks can set the agenda

The idea of a global regulatory framework for AI is bandied about much more often than it is scrutinised. And yet, one key lesson from the financial sector is that overlapping national regulatory bodies, with a remit based in law and the powers to investigate and punish organisations that transgress, is the closest humanity has ever come to controlling systems which, like AI, are both powerful and profitable.

 

Look no further than the cryptocurrency sector as it is dragged kicking and screaming into the regulatory capture of traditional banking, shedding the worst of its fraud, misdemeanour and exploitation of users as it goes.

 

Similarly, by approaching AI through the prism of the strict regulatory regime that they’ve been working in for years, the banking industry has already taken significant pre-emptive steps to prevent potential harms from occurring.

 

The world’s leading banks have already developed best practices that are well-suited to an AI-led future. Kitemarked security (to stop users from seeing one another’s data, as was the case with ChatGPT); a mixture of auditing and industrial safety standards; accreditation for practitioners (where now most AI developers have no training at all in ethical application; transparency and accountable coding); interdepartmental oversight so leaders get early warning when something is going wrong. And of course, there’s intense scrutiny by regulators and regular submissions of financial and other performance data.

 

All of these tools will be extended to AI deployment in banking use cases. The challenge - and opportunity - for banks is to embrace this publicly. Banks have no greater asset than trust. Getting ahead of this topic will enable them to build public confidence in their approach and set an example across the wider economy - potentially encouraging some of their corporate and SMB clients to embrace a similar mindset.

 

Seizing the initiative

Time is running out for industry leaders, policymakers and regulators to fill the governance vacuum and ensure that the pursuit of powerful AI proceeds with greater caution and consideration.

 

Getting artificial intelligence regulation right is a matter of imagination, resources and speed. The imaginative step from current banking best practice to include AI is a feasible one. Banks do not lack in resources. It’s time for banking leaders to seize the initiative, reaffirm their own commitments to (and internal standards) around responsible AI self-governance, and drive the public discourse around workable, industry-specific AI regulation.

Jayakumar Venkataraman, Managing Partner, Europe, Financial Services and Insurance at Infosys Consulting: 

“For almost a decade, banks have been operating at low rates because of the Bank of England keeping rates significantly low, impacting their ability to drive substantial revenue growth.

“The difficulty today is that we have a whole generation that has become accustomed to low interest rates and high borrowing. With the sudden switch back to high rates, borrowers will need to get used to servicing their level of borrowing at these high rates or reduce their borrowings to a manageable level. 

“During this period of adjustment, it is likely that customers may experience varying levels of financial discomfort and distress, meaning banks need to step up to the plate when it comes to leveraging the data and predictive capabilities of AI and ML to identify early signs of trouble. Banks must become proactive in reaching out to customers and supporting them through appropriate advisory and restructuring of their financial liabilities. Thereby providing empathetic customer experience, especially during these times of financial confusion.

“With the financial squeeze tightening for many consumers, banks need to ramp up investment when it comes to educating customers on how to manage their finances too. This includes informing customers on various borrowing options or alternatives that fit their personal situation. We can expect to see banks evolve from mere transactional hubs to centres of consultancy - advising on what options are available to restructure debt, and offering solutions better suited to individual cashflows and business needs.”

At the World Economic Forum this year, Microsoft CEO Satya Nadella said that Artificial Intelligence (AI) would become "mainstream" in "months, not years". Now that AI seems to have reached a point where it can permeate any discussion and any sector, and importantly, can be applied to a wide range of enterprise and consumer applications, we can be sure that tech giants will be looking to invest heavily in AI to remain competitive.

ChatGPT, a chatbot developed by OpenAI, has taken the internet by storm over the past month. Although it is not smart enough to replace humans yet, the bot can respond to natural language prompts and uses past conversation threads and information on the internet to reply to the user. A few days after its launch, more than a million people were trying out ChatGPT.

If an 'AI wars' between tech companies does occur in a bid to be the best, industry leaders providing the technology used to produce AI should reap solid benefits – and profits. Potentially even more than Microsoft or other major technology platforms.

Five major contributors to AI technology  

Nvidia (NVDA) is the world leader in artificial intelligence due to its quality product portfolio. The invention of graphic processing units (GPUs) in 1999 was an influential moment in the computing industry. GPUs are essential for AI to achieve its parallel processing capabilities, so Nvidia is still a key industry player decades later and is benefitting from the boom in conversation around ChatGPT.

Interestingly, ChatGPT currently runs on Nvidia's two-year-old A100 chip, not the latest H100 or 'Hopper', which was released late last year. The new chip will supposedly perform AI learning functions nine times faster than the A100 chip and output (the action of an AI responding to a question or other stimulus) 30 times quicker. It also promises 3.5 times better energy efficiency and three times lower total cost ownership. When ChatGPT adopts the new chip, it is clear its capabilities will improve significantly.

While Nvidia's revenue from gaming chips fell sharply in the third quarter from the impact the pandemic had on the video game industry and cryptocurrency bear market, data centre revenue grew by an impressive 31%. With the release of the Hopper H100 chips and the start of the AI wars, we expect Nvidia to produce significant returns in 2023.

ASML Holdings (ASML) manufactures lithography systems which are critical in microchip production and are heavily relied upon by Nvidia to produce their GPUs. ASML has a monopoly on a key technology used in manufacturing advanced semiconductors called extreme ultraviolet (EUV) lithography. After chip companies started producing semiconductors with new advanced specifications, there was a need for the extremely thin lasers that EUV technology contains. With AI continually making advancements, it will require chips that advance with it. ASML technology allows for these advancements and will no doubt increase purchases of its machines.

Taiwan Semiconductor Manufacturing Corporation (TSM) is the world's first dedicated semiconductor foundry and is another significant contributor to Nvidia’s GPU production, giving it a competitive edge. In Q4 of 2022, TSMC produced more than 56% of the world's semiconductors. The management team at TSMC stressed that its high-performance computing segment for AI customers is the reason for its optimism about the semiconductor market recovering in the second half of 2023.

Micron Technology (MU), a memory manufacturer, will benefit from the uptake of AI solutions due to the large amounts of memory and storage it requires. Currently, Micron’s results are in freefall, and earnings are likely to be negative over the next two quarters from the historic slump in PC sales and weakness in smartphones and consumer electronics, which unfortunately have outweighed the delicate supply-demand balance in the memory market.

With limited competition, Micron and SK Hynix have announced drastic cost reductions for 2023, which should help rebalance supply and demand in the second half of 2023. Additionally, Micron achieved technological leadership within its limited competition last year. In the past six months alone, Micron became the first memory manufacturer to release DRAM 1-beta chips and the first manufacturer of 232-layer NAND flash memory chips. As the current leader in this space, and thanks to other market players seemingly cutting back on production, Micron should benefit in the second half of 2023 and beyond as demand for memory-intensive AI servers dramatically increases.

Microsoft (MSFT) invested $1 billion (83m) in OpenAI in 2019, and now the cloud giant is reportedly in talks to invest another $10 billion (£8.34bn) in the company, which indicates great potential in this new and improved AI engine. Last week, Microsoft released the OpenAI service on its Azure platform, which allows developers to incorporate it into their software projects. In fact, Microsoft itself is looking to infuse its current software products, from Office to Bing, with ChatGPT capabilities.

Artificial Intelligence

Artificial intelligence (AI) is a revolutionary technology. According to a recent Gartner survey in 2019, 37% of the surveyed businesses had already adopted AI technology, and the numbers would have increased by now.

There's no surprise that this revolutionary technology has also penetrated the accounting sector. Artificial intelligence can be used to automate processes in the accounting sector. For example, AI can help you to manage cash flow more effectively by using predictive analytics. This will help you to make better decisions about buying inventory or paying bills on time. There are several such use cases of AI in the accounting industry, such as:

Automating Tedious Tasks

As AI becomes more advanced, it will have an increasingly large impact on the way we work. In accounting, there will be less need for human intervention when it comes to tedious tasks such as data entry and calculations.

For example, an AI programme can automatically categorise your expenses and organise them into different categories like "food" or "gas." This makes it easy to see what areas of your business cost the most and where you can save some cash. It also makes it easier to see how much money came in each month from each source so that you can plan accordingly for future expenses.

AI can take over these tasks and make them much more efficient, freeing up your time for more critical tasks like strategic planning or client meetings. The beauty of AI is that it's not just about speed—it's also about accuracy.

Automation saves your employees time, allowing them to focus on more critical tasks, such as decision-making and communicating with clients.

Identifying Fraud

Accounting fraud is not a new phenomenon. It has been around for centuries and has grown in sophistication over time. However, it is not always easy to detect fraud as it occurs. In addition, accounting fraud may be difficult to prove because of the complex transactions and records typically involved in such cases.

One of the ways AI can help identify fraud is by providing more accurate data analysis than humans alone can provide. Accounting fraud often involves manipulating data that is used for financial reporting purposes. This could include falsifying information or making adjustments that distort the information presented in financial statements, which investors and others use to decide whether to do business with a company or individual.

Using artificial intelligence with big data analytics tools can help identify patterns or anomalies in data sets that would be difficult for humans alone to see clearly or quickly before they could take action against those responsible for committing fraud against an organisation through their actions or omissions during their employment.

Enabling Clients To Track Their Money In Real-Time

Most clients of accounting firms have no idea where their money is going. This makes it difficult for them to manage their finances effectively and efficiently.

With the help of AI, though, clients can easily track their expenses in real-time and also keep track of what they spend on different things throughout the day or week, depending on how often they want updates on their finances from an app or website.

Electronic Signature

Electronic signatures are becoming increasingly common in accounting because they make sharing documents, signing contracts, and sending invoices easier.

Electronic signature has many benefits for accounting departments, such as:

Cloud Computing

Cloud-based accounting software has been around for years, but until recently, most people were unaware of its benefits. However, cloud-based accounting software has become increasingly popular over the past few years because it offers many benefits that cannot be found in traditional accounting software.

The benefits are so tempting that most accountants now use cloud accounting software for daily work. A recent survey indicates that over half the respondent accounting firms use cloud accounting to enhance project management functions and improve communications.

Some of the crucial benefits of cloud computing for accounting firms include:

Big Data Analytics

Big Data Analytics can help accountants improve their business processes by making better decisions based on data analysis. For example, an accountant may use a software tool that automatically analyses historical data about business transactions and identifies common trends among those transactions that might indicate fraud or errors in reporting. This information can be used to detect potential issues before they become problems.

One example of big data analytics used in accounting is when a company uses it for tax compliance. They might use it on employee-related data, such as salary information or employee pension contributions. The company could also use this information to calculate how much tax they owe or how much money they need to set aside for other taxes due during the year (such as corporation tax).

Final Thoughts

As the world moves at a rapid pace, businesses must keep up. The accounting sector has long relied on effective paper records management, but those days are quickly fading away. As digital storage becomes the norm, you can expect all kinds of advancements in this area – including AI analytics for business intelligence. After all, there's no sense in relying on outdated techniques when you have so much opportunity for growth available with emerging trends.

A new bar called AI Bar has a system that registers customers’ faces. It then lets the barman know which customer is next in line. You can use your fingerprint to unlock your phone. And many high-security offices now use a person’s body movements to determine their identity.

These systems have become so refined that critical identity verification moments don’t even get registered by the user's awareness. When you register yourself for a service, your face and eyes are matched with other data points you supply. Government-issued IDs like driving licenses and passports are matched with the biometric data that you have submitted.  If the match is successful, the system knows the customer is who they say they are. This entire seamless process can take as little as 8 seconds. Read on to find out more about the level of security biometrics offers.

What Exactly Is Biometrics?

Biometrics are slowly replacing traditional passwords and access keys everywhere. Biometrics can identify the unique physical qualities of a person. Facial features, the iris, fingerprints, and the retina are all such physical attributes. The Somali Army and Indian doctors have already adopted this technology at a state level. You have a piece of this technology in your pocket. Your smartphone can use biometrics to authenticate you into your bank account.

There are biometric technologies that can even peek underneath your skin. It can recognise the pattern of veins in your palm. When blood is deprived of oxygen in veins, it absorbs more infrared light than other tissues surrounding it. That is how your vein pattern can be recorded. New cutting-edge technology being developed allows a system to recognise a person based on their heartbeat. And you can even be recognised by your brainwaves.

So Is This A Goodbye To Passwords?

Fingerprints vs Passwords

Using biometrics is certainly more convenient. You simply touch a scanner with your finger, and you are in. It is a lot easier than typing in a password letter by letter. Passwords can also be weak, and they can be prone to hacking. They also happen to be out of date. However, password-protected systems are far easier to implement than biometrics. 

Facial Recognition vs Passwords

It all boils down to economics. The more data points that a system can log from your face, the more accurate your biometric profile will be. The level of security of the system will completely depend on its implementation. Thus, with more sensors, the system becomes more secure.

Iris Scanning vs Passwords

All these systems, whether fingerprints, facial recognition, or iris scanning, are similar. They all check for a single unique feature in a person. On the other hand, a password needs to be in your memory. You can’t just make a note of it and keep it somewhere because someone might find it. Furthermore, anyone who has your password can assume your identity. Thus, the future lies in multi-factor authentication. The most widely adopted systems will be those that users find the easiest to work with.

How Safe Is Your Biometric Data?

The responsibility of keeping your data secure rests with the company. In the ideal scenario, all biometric data is kept on the user’s device and not in the cloud. It makes things a lot harder to hack into. This practice is, however, not always followed.

A team of Israeli researchers hacked into a system with the biometrics of over 1 million individuals. They could gain access to 23 GB of data with 27 million unique data points. This set of data contained fingerprints, facial profiles, etc.

But password-based systems are also prone to hacking. Passwords can be stolen, and someone can watch you enter them, which isn’t possible with biometrics. Unfortunately, hackers have been quite successful in beating biometric systems. And unlike passwords, you can’t change your biometrics once they are compromised. Under lab conditions, hacking biometrics is possible.

An iPhone fingerprint scanner can be fooled by a fingerprint impression from a piece of glass. A Samsung phone's iris scanner can be fooled by using a contact lens. A computer club in Germany could bypass a palm vein scanner using a wax hand. A Chinese group was able to beat Apple’s face ID using a pair of regular glasses and tape.

As you can see, biometrics are not perfect yet. However, it all depends on the number of sensors in use and the economics. The more elaborate a system becomes, the more secure it becomes.

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