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The only 2 red countries in this open banking map: Vietnam and the Philippines.

Clearing the Confusion: AI vs Machine Learning vs Deep Learning Differences

Raise your hand if you’ve been caught in the confusion of differentiating artificial intelligence (AI) vs machine learning (ML) vs deep learning (DL)…

Bring down your hand, buddy, we can’t see it!

Although the three terminologies are usually used interchangeably, they do not quite refer to the same things.

Andrey Bulezyuk, who is a German-based computer expert and has more than five years of experience in teaching people how artificial intelligence systems work, says that “practitioners in this field can clearly articulate the differences between the three closely-related terms.”

Therefore, is there a difference between artificial intelligence, machine learning, and deep learning?

Here is an image that attempts to visualize the distinction between them:

As you can see on the above image of three concentric circles, DL is a subset of ML, which is also a subset of AI.

Interesting?

So, AI is the all-encompassing concept that initially erupted, then followed by ML that thrived later, and lastly DL that is promising to escalate the advances of AI to another level.

Let’s dig deeper so that you can understand which is better for your specific use case: artificial intelligence, machine learning, or deep learning.

What is artificial intelligence?
As the name suggests, artificial intelligence can be loosely interpreted to mean incorporating human intelligence to machines.

Artificial intelligence is the broader concept that consists of everything from Good Old-Fashioned AI (GOFAI) all the way to futuristic technologies such as deep learning.

Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), such an “intelligent” behavior is what is called artificial intelligence.

For example, such machines can move and manipulate objects, recognize whether someone has raised the hands, or solve other problems.

AI-powered machines are usually classified into two groups — general and narrow. The general artificial intelligence AI machines can intelligently solve problems, like the ones mentioned above.

The narrow intelligence AI machines can perform specific tasks very well, sometimes better than humans — though they are limited in scope.

The technology used for classifying images on Pinterest is an example of narrow AI.

What is machine learning?
As the name suggests, machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”.

The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions.

ML is a subset of artificial intelligence; in fact, it’s simply a technique for realizing AI.

It is a method of training algorithms such that they can learn how to make decisions.

Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information.

For example, here is a table that identifies the type of fruit based on its characteristics:

As you can see on the table above, the fruits are differentiated based on their weight and texture.

However, the last row gives only the weight and texture, without the type of fruit.

And, a machine learning algorithm can be developed to try to identify whether the fruit is an orange or an apple.

After the algorithm is fed with the training data, it will learn the differing characteristics between an orange and an apple.

Therefore, if provided with data of weight and texture, it can predict accurately the type of fruit with those characteristics.

What is deep learning?
As earlier mentioned, deep learning is a subset of ML; in fact, it’s simply a technique for realizing machine learning. In other words, DL is the next evolution of machine learning.

DL algorithms are roughly inspired by the information processing patterns found in the human brain.

Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines.

The brain usually tries to decipher the information it receives. It achieves this through labelling and assigning the items into various categories.

Whenever we receive a new information, the brain tries to compare it to a known item before making sense of it — which is the same concept deep learning algorithms employ.

For example, artificial neural networks (ANNs) are a type of algorithms that aim to imitate the way our brains make decisions.

Comparing deep learning vs machine learning can assist you to understand their subtle differences.

For example, while DL can automatically discover the features to be used for classification, ML requires these features to be provided manually.

Furthermore, in contrast to ML, DL needs high-end machines and considerably big amounts of training data to deliver accurate results.

Wrapping up
Do you now understand the difference between AI vs ML vs DL?

Then, raise your hands…

The legendary investor of the Silicon Valley talking about his investing theories in his book – Zero to One.

https://youtu.be/rFZrL1RiuVI

https://www.businesswire.com/news/home/20181114006003/en/CIMB-Group-Joins-RippleNet-Power-Instant-Payments

CIMB is one of the first banks to leverage blockchain technology to tap into region’s USD120 billion remittance business

Ripple’s CEO Brad Garlinghouse and CIMB Group’s CEO Tengku Dato’ Sri Zafrul Aziz celebrate their partnership. (Photo: Business Wire)
Ripple’s CEO Brad Garlinghouse and CIMB Group’s CEO Tengku Dato’ Sri Zafrul Aziz celebrate their partnership. (Photo: Business Wire)

November 14, 2018 08:06 PM Eastern Standard Time
SAN FRANCISCO–(BUSINESS WIRE)–CIMB Group (“CIMB” or “the Group”) and Ripple have entered into a strategic collaboration to enable instant cross border payments across its various markets. On the back of this partnership, CIMB will join Ripple’s network (“RippleNet”), which will facilitate access to other RippleNet members and allow CIMB to grow its cross border payments business.

“We’re seeing banks and financial institutions from across the world lean into blockchain solutions because it enables a more transparent, quicker and lower cost payments experience”

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Ripple’s blockchain-based solution has been deployed to enhance Speedsend, CIMB’s proprietary remittance product. This will expand CIMB’s Speedsend network and open new payment corridors to improve consumer access to cross-border remittances, both inbound into ASEAN and outbound to other countries. The solution is now live on Speedsend, enabling remittances via corridors such as Australia (in partnership with InstaReM, also a member of RippleNet), USA, UK and Hong Kong.

“We are delighted to be part of RippleNet and look forward to a fruitful partnership with Ripple by leveraging each other’s strengths and capabilities. This innovative blockchain solution will revolutionise international cross-border remittances, and is a testament to CIMB’s ongoing efforts to enhance its digital banking proposition by providing speedy and cost-efficient solutions to our customers across ASEAN,” said Tengku Dato’ Sri Zafrul Aziz, CEO, CIMB Group.

As part of the overall partnership roadmap, CIMB intends to extend the solution to other use cases across the Group. There is a growing demand for cross border payment solutions, with the World Bank projecting that remittances to Southeast Asia will grow to USD120 billion by the end of 2018.

“We’re seeing banks and financial institutions from across the world lean into blockchain solutions because it enables a more transparent, quicker and lower cost payments experience,” said Brad Garlinghouse, Ripple CEO. “CIMB’s network already spans 15 countries, nearly 800 branches and offers Speedsend – one of the best solutions in the ASEAN region. Now, by integrating Ripple’s blockchain technology, they will enable their customers to send vital funds to family, friends and loved ones more efficiently. With its focus on innovation, CIMB will continue to be a dominant force in the region for years to come.”

About Ripple

Ripple provides one frictionless experience to send money globally using the power of blockchain. By joining Ripple’s growing, global network, financial institutions can process their customers’ payments anywhere in the world instantly, reliably and cost-effectively. Banks and payment providers can use the digital asset, XRP, to further reduce their costs and access new markets. With offices in San Francisco, New York, London, Luxembourg, Mumbai, Singapore and Sydney, Ripple has more than 100 customers around the world.

About CIMB Group

CIMB Group is one of ASEAN’s leading universal banking groups and is Malaysia’s second largest financial services provider, by assets. It offers consumer banking, commercial banking, investment banking, Islamic banking and asset management products and services. Headquartered in Kuala Lumpur, the Group is now present in 9 out of 10 ASEAN nations (Malaysia, Indonesia, Singapore, Thailand, Cambodia, Brunei, Vietnam, Myanmar and Laos). Beyond ASEAN, the Group has market presence in China, Hong Kong, India, Sri Lanka, Korea, the US and UK.

CIMB Group has the most extensive retail branch network in ASEAN of around 800 branches as at 30 September 2018. CIMB Group’s investment banking arm is also one of the largest Asia Pacific-based investment banks, offering amongst the most comprehensive research coverage around 700 stocks in the region.

CIMB Group operates its business through three main brand entities, CIMB Bank, CIMB Investment Bank and CIMB Islamic. CIMB Group is also the 92.5% shareholder of Bank CIMB Niaga in Indonesia, and 94.1% shareholder of CIMB Thai in Thailand.

CIMB Group is listed on Bursa Malaysia via CIMB Group Holdings Berhad. It had a market capitalisation of approximately RM 56.3 billion as of 30 September 2018. The Group has around 36,000 employees located in 15 countries.

Contacts
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Tom Channick
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Suria Zainal
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Here are the most iconic moments from the last 10 years in payments.

01 The global financial crisis hits

One of the decade’s most stand-out moments took place on Sunday, Sept. 14, 2008. On that day, thousands of payments professionals from banks and technology companies flew into Vienna to join the Swift International Banking Operations Seminar (SIBOS), a flagship annual banking and financial conference.

Many arrived that Sunday only to find message after message asking them to call back the office to discuss the collapse of Lehman Brothers. The next day most of the conference attendees turned right around and flew out of Vienna to return to their offices. By the end of that week, the banking system was changed forever.

Not only had Lehman failed, but Washington Mutual had been absorbed by JPMorgan Chase, the Royal Bank of Scotland collapsed, and many other banks struggled to stay afloat. The result has been a swathe of regulations that have tightened up the banking system and raised required capital levels to the highest ever. Equally, the result was a major tightening of loans and credit lines, with many credit cards moving into default. U.S. banks wrote off more than $100 billion in credit-card loans over the next two years after the crisis hit and, interestingly enough, credit card levels are back at pre-crisis highs today. Could there be another 2008 on the horizon?

02 The growth of mobile social

In the early part of this decade, most people were just hearing about social media. Yet, Facebook and Twitter were rising as social media stars, and even MySpace was still riding high back then. Equally, the idea of a smartphone had only just appeared the year before, with the launch of the first Apple iPhone in 2007. Remember when no one thought that it would succeed because it didn’t have a keyboard?

What the Nokias and Blackberrys of this world missed, however, is that the iPhone is not a phone but a computer. And once you combine that with social media, it’s a recipe for an addicted consumer base living in a world where we talk less and stare at a tiny screen more. It has also led to a massive wave of payments transformation, with every bank in the world offering mobile banking services and seeing more interaction through mobile than with any other media or channel.

03 The app revolution

Combining the mobile social with a killer app was another real change, especially for those of us in this industry. Apps led to mobile wallets being launched in almost every nation from America to Asia to Africa. The result today is leaders like Alipay and WeChat Pay dominating the payments landscape across China, with $15 trillion transacted last year through the country’s mobile wallets alone.

In the United States, we of course had PayPal growing quickly with email and web-based payments. But what no one could have predicted was the rise of APIs and apps like Braintree and Venmo, now part of the ever-growing PayPal empire along with other strategic acquisitions. In fact, no one would have expected that PayPal would be worth three times more than its parent company, the mighty eBay, a decade later. (It was spun off as a separate entity in 2015.)

Adding payment systems to mobile apps made for a dramatic change. From Square in the United States to iZettle in Europe, mobile dongles have converted any device into a point of sale, and it’s predicted that mobile phones will process more than $55 trillion in payments in 2024, according to Global Market Insights Inc.

04 Pan-European movement — and a laggard

Europe was working toward adopting a single currency in 2008, with regulations for the euro driving changes across all aspects of finance. One was the first iteration of the Payment Services Directive, bringing with it efforts to harmonize all of the payments products used in 28 nations. To make this happen, the banks were working co-operatively through the Euro Banking Association (EBA) to build the Single Euro Payments Area (SEPA). This would allow standing orders, direct debits, credit transfers and any form of payment to move across Europe’s countries as though they were domestic payments.

Meanwhile, over in the United States, a different type of herculean effort was unfolding — the move from magnetic stripes to EMV. Ten years after the UK, the United States finally fully mandated chip-and-PIN cards. Much like the Eurozone, it experienced growing pains with getting all players on the same page. Even today, fractures remain, with some merchants opting to take the liability risk associated with mag-stripe cards rather than making the switch to chip.

05 Building banks with code

One of the biggest changes that took place during this decade was the groundwork for open banking — the open sourcing of banking structures such that anyone with an app or API could play. That change was being driven by cloud-based services and platforms like Amazon, Microsoft’s Azure, Google’s Cloud, and Tencent and Alibaba cloud services in China.

These cloud services allow start-ups to ‘start up’ easily, at a very low cost. For example, in 2010, the Collison brothers wrote seven lines of code to make checkout easy online. These seven lines of code are now valued at more than $30 billion, as this code is used by Apple, Uber, Kickstarter and many other leading innovative online firms. I’m sure you guessed that this code is called Stripe, and it’s taken over the online world in just a few years. (Case in point being the list of heavy hitters above.) Why? Because its simple integration technologies and easy merchant onboarding solve some of the biggest known problems in the payment processing space.

06 The launch of fintech

A decade ago, there was no big ‘fintech.’ There were just a few companies starting out on a road of opening financial systems with software. Today, there are tens of thousands of companies working in this space and each of them focusing on doing one thing really well, like Stripe, Klarna and Adyen. They aren’t trying to boil the ocean, doing everything, but instead are focused on one fundamental area and developing code to solve the issues in that area.

From there, we have seen many other powerful brands emerge, including the rise of fintech unicorns — firms that launched since 2000 and are now valued at more than $1 billion — including names such as SoFi, Kabbage and Robinhood. These are financial software companies that dominate the world of money, with humble origins based upon code. And many of these companies are now global, not just local. In fact, many are starting to come together, like TransferWise and Wirecard, to form regional and global giants.

07 Satoshi Nakamoto and the bitcoin

Another milestone moment came when a mysterious figure (who has never been identified) Satoshi Nakamoto, posted a PDF online called “Bitcoin: A Peer-to-Peer Electronic Cash System.” The white paper posted in October 2008, with the opening line stating that a “purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution.” This led to the phenomena of bitcoin, also launched by Nakamoto in January 2009. It is interesting to see that, over the last 10 years, bankers continually say that bitcoin is unnecessary and purely exists for criminal activities. Yet it doesn’t go away (even though its value rises and falls faster than a certain world leader can tweet).

08 Distributed Ledger Technology (DLT)

Interestingly, Nakamoto’s paper doesn’t mention the blockchain and instead only talks about time-stamped blocks that are added to the database and then cannot be changed. Yet it is this time-stamped, irrevocable, tamper-proof database ability on the internet that has payments geeks excited, and others too. A tamper-proof internet-based database could be shared with everyone, including people you don’t trust. Once they realized this, banks started developing new ideas and infrastructures based upon it.

It also led to lots of confusion. Now we even have blockchain-inspired developments (like Corda for the banking industry) that don’t use a blockchain. Similarly, we have cryptocurrencies, like Ripple’s XRP, that are completely unnecessary to implement the advantages of their payments protocol. In other words, you can have blockchain-like systems that don’t need cryptocurrencies or blockchain to exist, which is why everyone today talks about Distributed Ledger Technology. Either way, these all evolved from the concepts articulated and implemented by the bitcoin army. In the end, that means we have Satoshi and his crowd to thank for creating this wave of change.

09 Leapfrogging old legacies

What all of these milestones have in common is that the world is changing fast. For example, just as Visa had its initial public offering and left the arms of its former bank owners, the card networks found themselves needing to make room for the mobile network. Just as the mobile networks embrace change, thanks to support from Apple and Google in the form of Apple Pay and Samsung Pay, contactless payments are replaced by QR-code payments such as WeChat Pay, Alipay and PayTM. The reason for the fast rise of electronic payment systems in new markets like China and India is that merchants can take these payments without any special equipment. Hence, as the West grapples with card technologies, the lengthy rollout of chip and PIN and the attempt to create contactless cards and systems, the developing world has leapfrogged us all by moving to simplified mobile-centric systems.

In fact, the real challenge today is for Visa and Mastercard to evolve their old legacies and renew and refresh. They are doing this, but the infrastructures developed for Chinese tourists around WeChat Pay and Alipay using QR codes could just as easily be used by American and European tourists. And there is a strong incentive for them to do just that, as the systems are built around merchants getting more business through personalized offers to consumers as they walk past the store. That’s a good reason to use an app.

10 The race to cashless

And then, throughout all of this history, is the often-discussed ‘war on cash.’ Mobile wallets, digital currencies, peer-to-peer payments and more are all focused upon getting rid of that physical cash in your wallet. Some countries are seeing stunning success in this, with Turkey, Sweden and China all anticipating that they will be cashless within the next decade.

Eliminating cash is good for governments, of course. After all, the more automated the payments going through the systems, the easier they are to track and trace. Electronic payments allow people to avoid cash and the insecurities that go with it, such as getting mugged. And let’s not even get started on the bacteria (and other undesirable materials) carried on cash. The rise of a cashless society is upon us and much of it is down to the major developments of the past decade.

In the end

The seeds of all of these things — mobile wallets, the blockchain, Chinese leapfrogging payments, pan-European structures, an African revolution and the rise of a new American payments giant — were all being laid in 2008. As for the next 10 years? I believe we will see even more transformation through biometrics, embedding payments into devices through the internet of things, the launch of a global digital currency and the rise of code.

A great way to understand the future priorities for a company is to see where they invest resources. When you look at where Toyota, the Japanese industry giant, has recently invested, it’s clear the company is preparing to remain relevant and competitive in the 4th industrial revolution as a result of its investments and innovation in artificial intelligence, big data and robots.

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Toyota AI Ventures Invests in AI Start-ups

With initial funding of $100 million, Toyota AI Ventures invests in tech start-ups and entrepreneurs around the world that are committed to autonomous mobility, data and robotics. Toyota’s investments help accelerate getting critical new technologies to market. One of the organization’s investments is in May Mobility, a company that is developing self-driving shuttles for college campuses and other areas such as central business districts where low-speed applications are warranted. This is just one of the services that could blaze a trail to fully autonomous vehicles of the future.

Additionally, Silicon-based Toyota AI Ventures contributed funding as well as mentorship, incubation facilities and validation to Nauto, a company that’s creating a shared data platform to prevent accidents caused by distracted driving; SLAMcore, a visual tracking and mapping algorithm developer for smart tech; Intuition Robotics, an organization that creates social companion technologies that are accessible and intuitive for seniors;Boxbot, a company that’s building self-driving delivery robots; and more. Like many disruptors, Toyota AI Ventures seeks out other innovators to tackle important challenges to propel the latest technologies.

AI Enhancements to Automobiles

Innovation has always been omnipresent at Toyota from its earliest days and it’s clear the company is continuing that innovative tradition. While Toyota was originally a company that produced wooden hand looms, the majority of people know the company for its automobile division. Their aim is to use artificial intelligence (AI) technology to make “cars an object of affection again” as soon as 2020 and is investing $1 billion in self-driving cars and AI between now and then to achieve it. Through Toyota’s investments in tech start-ups such as Perceptive Automata it hopes to create the technology to allow autonomous vehicles more human-like intuition when they are on the road more similar to how human drivers interact with pedestrians.

Toyota’s Concept-i fully electric autonomous vehicles demonstrate the company’s “Learn, Protect, Inspire” philosophy. The Concept-i artificial intelligence system, nicknamed “Yui,” learns about its driver by listening to conversations, monitoring social media activity and schedules, and analyzing facial expressions and driving habits to sense when a driver might be sleepy or stressed or what might enhance the driver’s comfort and then adjusts lighting, music or to the seats accordingly. The full vision is “intelligent talking cars” where cars equipped with AI can have conversations back and forth with passengers.

Toyota also announced the Concept-i Ride for users with wheelchairs or other disabilities and the Concept-i Walk, that’s designed for pedestrians to use on sidewalks similar to a Segway. Both additions to the Concept-i product line feature the same sensing abilities as the Concept-i vehicle.

In an effort to bring AI efficiency to ridesharing, Toyota has collaborated with Japan Taxi to test out its new AI-propelled taxi dispatch system. Not only does the company expect to enhance Tokyo’s taxi service, company leaders believe the driving data and real-time video will be instrumental in boosting efficiency and profits.

Toyota and Robotics

As a result of deep learning technologies and the millions of images uploaded to the internet via social media and other websites, machines are getting better at being able to “see” like a human. Toyota’s Human Support Robot (HSR) algorithms help it understand the world and can be life-changing for individuals with impaired mobility. The company also announced a T-HR3 humanoid robot, a third-generation version that uses an immersive “Master Maneuvering System” that mirrors a human user’s movements or can operate autonomously to support humans in a variety of settings.

Toyota is also innovating warehouse logistics with automation and artificial intelligence for lean approaches to material handling. With AI on board every vehicle, the future of logistics is when the right truck or piece of equipment—pallet drone, mid-lifter, ultra-lifer—is used for the right task every time. The company’s innovations aim to minimize energy use, prevent delays and eliminate waste thanks to teamwork between trucks, lifts, loaders and other equipment.

Toyota is a company that’s worth paying attention to as it progresses further into the 4th industrial revolution with investments in AI, big data and robotics.

by Benard Marr on Forbes