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How to Explain AI, Machine Learning and Natural Language Processing – ReadWrite



How to Explain AI, Machine Learning and Natural Language Processing - ReadWrite

Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) are three of the most powerful technologies that our modern society has access to. They can process data in huge quantities in a way that no human being could hope to achieve, and they will revolutionize the way we look at every aspect of our lives.

At the same time, they can be pretty complicated to understand, especially for people who aren’t used to working with new technologies.

The problem is that you can’t just bury your head in the sand and hope that AI, ML, and NLP will go away. Because society will move on without you and you’ll end up getting left behind.

How to Explain AI, Machine Learning and Natural Language Processing

The good news is that as long as you use simple language and accessible examples, there’s no reason why you can’t explain them to even the most old-fashioned and tech-averse people in your company.

Your accessibility to the explanations is important because without encouraging other people at your company to buy into new technologies, you’re not going to be able to roll them out across your company.

In fact, these three technologies are already so pervasive that it’s no longer just useful to know about them. It’s mandatory.

With that in mind, let’s take a closer look at AI, ML and NLP, along with their implications for you and your business.

How to explain AI?

Artificial intelligence (AI) is the use of technology to mimic the human brain. Normally, computers and algorithms function by responding to human input and following a set of rules programmed into them when they were first developed.

Artificial intelligence is a little different in that it’s designed to work more like a human being.

For example, let’s use an algorithm that looks at photos to determine whether they show a cat. A traditional algorithm might follow a set of criteria, looking for whiskers or for cat ears, and it might get tricked by someone dressed up for a fancy dress party.

In contrast, an AI algorithm would be provided with thousands of pictures of cats and left to its own devices. It would form its own conclusions of what a cat looked like and be able to function much more like a human being. After all, do you look at a cat and run through a checklist to determine whether it is actually a cat? Or do you just know what a cat looks like?

AI — a prediction machine

Artificial intelligence algorithms have also been called “prediction machines,” and the reason for that is that they essentially predict what a human might think or do in any given situation.

That’s actually how self-driving cars work. They don’t have a ton of different algorithms telling them what to do, but rather they’ve analyzed millions of miles of human driving and use that to make predictions on what a human driver would do.

By functioning as a prediction machine and making calculations at an unbelievably rapid rate.

That fast prediction machine and calculations is why AI algorithms can drive cars and or better than human drivers. In fact, some future thinkers suggest that human-driven cars will eventually become illegal as they won’t be as safe as self-driving cars.

How to explain ML?

Machine learning is essentially the next step up from artificial intelligence, although the two of them are similar and often used in conjunction.

The idea behind machine learning is to provide huge amounts of data to an algorithm to draw its own conclusions based on the data.

Machine learning typically requires much less steering than AI, often because the programmers don’t actually know what the algorithm will discover.

Moving back to the example of an algorithm to identify images of cats, an AI algorithm would be fed thousands of images of cats and instructed to identify commonalities.

A machine learning algorithm would be fed millions of unsorted images and would decide for itself that there were similarities between the photos of cats.

It’s machine learning that powers’ Netflix’s recommendations system, an algorithm known for its power and accuracy.

By analyzing all of its users’ viewing data, Netflix can make super-personalized recommendations for people based on what other, similar users enjoyed. Amazon does something similar with its product recommendations.

What’s particularly interesting about machine learning is that it gets more and more powerful as it gets access to more and more data. It’s a bit like the opposite of diminishing returns, an impressive snowball effect that acts as a gift that keeps on giving.

Machine learning, then, underpins many of the apps and tools that we use daily, and it’s only going to get more and more common as time continues to tick by.

Perhaps that’s no surprise, given the rapid pace at which technology is developing alongside the huge amount of data we’re creating daily.

With so much data and so many disparate systems, machine learning isn’t just nice to have — it is becoming more and more essential.

In many cases, it’s the glue that holds other systems together, and we just couldn’t function without it. In the future, it will only get more and more important to our society, powering everything from our healthcare systems to smarter cities.

How to explain NLP?

Natural language processing is a subset of AI and machine learning that focuses specifically on enabling computers to process and understand human language.

Every time you ask Alexa a question, she’s using natural language processing to understand the context of what’s being said. Then she uses it again when she formulates a response that human beings can understand.

A response that a human can understand makes natural language processing a powerful tool because it basically acts as an interface between humans and robots, bridging the gap between the two.

NLP powers everything from Google’s search engine to commercial chatbots (like zfort dot com and when it’s done well, you won’t even notice that it’s there.

NLP often gets overlooked when compared to AI and machine learning, perhaps because the other two have more “glamorous” (supposedly) uses.

Remember this:

People forget those same algorithms for AI and ML wouldn’t work without NLP. If AI and machine learning are the engines that sit beneath the bonnets of future tools, NLP is the ignition.

Natural Language Processing (NLP) is an interface between humans and machines, essentially allowing us both to talk the same language.

Being the interface is important because AI and machine learning can only work if they have access to data. Natural language processing can help them understand human speech and handwriting.

The ability to translate – NLP

NLP can even be used to take data from one source and translate it into data that another source can read.

The ability to translate into a usable source is what makes natural language processing just as important as artificial intelligence and machine learning. They all work well together to form a smart ecosystem where the different technologies work together to support each other.

Because it’s still relatively early days for AI, ML and NLP, we’re likely to see even more powerful combinations in the future.


Now that you know the fundamentals behind artificial intelligence, machine learning, and natural language processing — you have a new job now. It’s up to you to share what you’ve learned today with the people that you work with.

Remember that it’s important to know about these technologies even if you’re not actively using them because they’re the defining tech trends of our generation.

Believe it! These three technologies are going to revolutionize everything. Knowing what machine learning is today is like knowing what the internet was in 1998.

It’s not enough for just one person in your company to understand this new tech. Your entire company needs to be familiar with these tech trends so that you can have high-level discussions and make important strategic decisions based on knowledge and information and not just gut instinct.

Fortunately, with the information that we’ve shared with you today, you should know more than enough not only to understand AI, ML and NLP. Now, go forward and teach those within your influence — other people need to know and understand the details.

Build allies in your company and business so that you have backing as you drive your business into the future.

Above all else, remember that these new technologies are already a part of our lives and they’re very much here to stay.

They’ve proved their usefulness, and as technology continues to improve and to come down in price, they’ll only become more and more important.

Here’s hoping you successfully convince your co-workers of the software’s potential. Good luck.

Image Credit ivan samkov; pexels; thank you!

Andrew Mikhailov

From 2017 as a CTO at Zfort Group, Andrew concentrates on growing the company into the areas of modern technologies like Artificial Intelligence, BigData, and IoT. Being a CTO, Andrew doesn’t give up programming himself because it is critical for some of the projects Andrew curates as a CTO.


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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