08 Dec

Microsoft ditching their Edge browser to use Chrome: The true story behind the rumor

Rumors say: Microsoft ditching their Edge browser to use Chrome.

The true story: Microsoft is not giving up on Edge.

Quite the opposite — as a result of the change Edge will probably become available for Macintosh and Linux. Microsoft is giving up on EdgeHTML, which is the rendering engine that Edge uses. All rendering engines do exactly the same thing: they produce a DOM (document object model) from an HTML string according to the HTML5 specification.

When Microsoft replaces EdgeHTML with Chromium, there will be no change in the user experience, except that all the Google fanboys will have no basis for whining. Not that they ever let facts slow them down.

Microsoft is not building a browser based on Chrome.

Chromium is not Chrome. It’s FOSS with a licence that removes Google’s legal and technical opportunities for sabotage, developed in the open. Using it makes it straightforward to port Edge to all the platforms for which Chromium is available.

New features appear first in Chromium, and the better ones eventually appear in Chrome. If Edge replaces EdgeHTML with Chromium, new features and bugfixes will consistently be available in Edge before they appear in Chrome.

So the outcome of this will be

  • Cross platform availability of Edge
  • Perfect compatibility with the tested scenarios
  • Edge will be first to market with all the new toys
  • Reduced development costs and lead times for Microsoft

Straight from Microsoft:

25 Nov

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

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.


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…

17 Nov

ASEAN’s 120 Billions Remittance Opportunity and how CIMB wants to get it with Ripple


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.

Tom Channick
CIMB Group
Suria Zainal