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The Future of AI – 7 Stages of Evolution You Need to Know About – ReadWrite



The Future of AI - 7 Stages of Evolution You Need to Know About - ReadWrite

According to artificial intelligence statistics, the global AI market is expected to grow to $60 billion by 2025. Global GDP will grow by $15.7 trillion by 2030 due to artificial intelligence, as it will increase business productivity by 40%. Investment in artificial intelligence has grown by 6 times since 2000. In fact, 84% of businesses think that artificial intelligence can give them a competitive advantage.

The Future of AI – 7 Stages of Evolution You Need to Know About

If you are a fan of science fiction movies, you might have seen AI in action in its full glory. With artificial intelligence leaving impressionable marks on every facet of our personal and professional lives, it is important to understand how it works and how it will evolve in the future. This allows us to prepare for the future in a much better way.

In this article, you will learn about how artificial intelligence will evolve in the future and what stages it will go through.

7 Stages of AI Evolution

  1. Rule-Based Systems

This form of artificial intelligence is everywhere. It surrounds us whether we are at work, at home or traveling. From business software to smart apps, aircraft to electronic appliances, all follow rule-based systems. Robotic process automation is the next stage of a rule-based system in which the machine can perform complete processes on their own without requiring any help from humans.

Since it is a basic level of artificial intelligence and also the most ubiquitous, it is cost-effective and fast. That is why mobile app development company uses it. On the flip side, it requires comprehensive knowledge and domain expertise and involves some kind of human involvement. Generating rules for such a system is sophisticated, time-consuming and resource-intensive.

  1. Context Awareness and Retention

This type of algorithm is developed by feeding information about a particular domain in which they would be implemented in. Since these algorithms are trained using the knowledge and experience of experts and are updated to cope with new emerging situations, this makes them an alternative to human experts in the same industry. One of the best examples of that type of artificial intelligence is smart chatbots.

Chatbots have already changed the way businesses look at customer support and deliver customer service. It has not only saved businesses from hiring customer service representatives but also help them automate and streamline customer support. In addition to this, it can help businesses in many other ways.

  • Improve customer satisfaction
  • Collect useful customer data
  • Increase sales
  • Save money
  • Respond to customer queries quickly
  • Ensure 24/7 availability

Another form of this type of artificial intelligence is Robo advisors. These Robo advisors are already being used in finance and helping people make sensible investment decisions. We might see their applications grow in other industries as well in the future. They can automate and optimize passive indexing strategies and follow mean-variance optimization.

  1. Domain-Specific Expertise

Unlike context-aware and retention artificial intelligence, domain-specific expertise aims to not only reach a level of human capability but also want to surpass it. Since it has access to more data, it can make better decisions than its human counterpart. We already see its application in areas of cancer diagnosis.

Another popular example of this type of AI is Google’s Deepmind Alpha Go. Initially, the system was taught rules and objectives of winning and later, it taught itself how to play Go. The important thing to note here is that it did so with human support, which stopped them from making poor decisions. In March 2016, we finally saw Alpha Go defeat the 18-time world Go champion Lee Sedol by four games to one.

Soon after Alpha Go’s success, Google created Alpha Go Zero, which requires no human support to play Go. It learned rules and analyzed thousands of Go games to create strategies. After three days, it defeated Alpha Go by a huge margin of 100 games to nil. This was a clear indication of the potential of smart machines and what they can do when they acquire human-like intelligence. It was a massive breakthrough in the field of artificial intelligence.

  1. Reasoning Machines

These reasoning machines are powered by algorithms that have a theory of mind. This means that it can make sense of different mental states. In fact, they also have beliefs, knowledge, and intentions — which are used to create their own logic.

Hence, they have the capacity to reason, negotiate, and interact with humans and other machines. Such algorithms are currently at the development stage, but we can expect to see them in commercial applications in the next few years. Due to this, they can interact, reason, and even negotiate with humans as well as other machines.

  1. Self Aware Systems

The ultimate goal of artificial intelligence is to create systems that can surpass human intelligence. Even though we are very close to achieving that goal, there is still no system that can achieve that feat. Experts are divided on this one as some think that we can achieve that level in less than five years while others argue that we may never be able to achieve that level.

Self-aware AI systems will have more perspective and can understand and react to emotional responses. Just like self-aware humans, self-aware machines can also show a degree of self-control and can regulate themselves according to the situation.

  1. Artificial Superintelligence

AI researchers have already developed systems that can win from humans in games and do a better job in many other areas. What’s next? The real challenge for AI experts would be to create AI-powered systems that can outperform humans in every department. As a human, visualizing something which is miles ahead of us is out of the question, let alone creating it.

If AI researchers succeed in creating something along these lines, we might see it is being used in solving the world’s biggest problems, such as poverty, hungry and climate change. In fact, we can also expect such systems to make new scientific discoveries and design new economic and governance models. Just like self-aware systems, experts are on the fence about whether it is possible or not. Even if it is possible, how long will it take for this dream to see the light of the day?

  1. Singularity and Transcendence

At this stage of artificial intelligence, we will be able to connect our brains with one another. This will pave the way for the future of the internet. This will not only help with traditional activities such as sharing ideas but also help with advanced activities such as the ability to observe dreams. It could enable humans to communicate with other living beings, such as plants and animals.

How will artificial intelligence evolve in years to come? Share your opinion with us in the comments section below.

Muneeb Qadar

Working in digital marketing with mobile app development company in Dallas Branex, Muneeb Qadar Siddiqui has earned 8 years of experience with skills in digital marketing. Paid marketing, affiliate marketing, search engine marketing and search engine optimization are his strengths. He is also a connoisseur of fine dining in his free time. Do connect with him:
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Fintech Kennek raises $12.5M seed round to digitize lending



Google eyed for $2 billion Anthropic deal after major Amazon play

London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs



Deanna Ritchie

As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations



Deanna Ritchie

As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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