Intel IoT Revolution for Malaysia Industry 4.0

Intel rocks!

I have been following Intel for a few years now, a big part was for their push of the 3D Xpoint technology. Under the Optane brand, Intel has several forms of media types, addressing persistent memory to storage class and solid state storage. Intel, in recent years, has been more forefront with their larger technology portfolio and it is not just about their processors anymore. One of the bright areas I am seeing myself getting more engrossed in (and involved into) is their IoT (Internet of Things) portfolio, and it has been very exciting so far.

Intel IoT and Deep Learning Frameworks

The efforts of the Intel IoTG (Internet of Things Group) in Asia Pacific are recognized rapidly. The drive of the Industry 4.0 revolution is strong. And I saw the brightest spark of the Intel folks pushing the Industry 4.0 message on homeground Malaysia.

After the large showing by Intel at the Semicon event 2 months ago, they turned up a notch in Penang at their own Intel IoT Summit 2019, which concluded last week.

At the event, Intel brought out their solid engineering geeks. There were plenty of talks and workshops on Deep Learning, AI, Neural Networks, with chatters on Nervana, Nauta and Saffron. Despite all the technology and engineering prowess of Intel was showcasing, there was a worrying gap.

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Storage Performance Considerations for AI Data Paths

The hype of Deep Learning (DL), Machine Learning (ML) and Artificial Intelligence (AI) has reached an unprecedented frenzy. Every infrastructure vendor from servers, to networking, to storage has a word to say or play about DL/ML/AI. This prompted me to explore this hyped ecosystem from a storage perspective, notably from a storage performance requirement point-of-view.

One question on my mind

There are plenty of questions on my mind. One stood out and that is related to storage performance requirements.

Reading and learning from one storage technology vendor to another, the context of everyone’s play against their competitors seems to be  “They are archaic, they are legacy. Our architecture is built from ground up, modern, NVMe-enabled“. And there are more juxtaposing, but you get the picture – “We are better, no doubt“.

Are the data patterns and behaviours of AI different? How do they affect the storage design as the data moves through the workflow, the data paths and the lifecycle of the AI ecosystem?

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The Heart of Digital Transformation is …

Businesses have taken up Digital Transformation in different ways and at different pace. In Malaysia, company boardrooms are accepting Digital Transformation as a core strategic initiative, crucial to develop competitive advantage in their respective industries. Time and time again, we are reminded that Data is the lifeblood and Data fuels the Digital Transformation initiatives.

The rise of CDOs

In line with the rise of the Digital Transformation buzzword, I have seen several unique job titles coming up since a few years ago. Among those titles, “Chief Digital Officer“, “Chief Data Officer“, “Chief Experience Officer” are some eye-catching ones. I have met a few of them, and so far, those I met were outward facing, customer facing. In most of my conversations with them respectively, they projected a front that their organization, their business and operations have been digital transformed. They are ready to help their customers to transform. Are they?

Tech vendors add more fuel

The technology vendors have an agenda to sell their solutions and their services. They paint aesthetically pleasing stories of how their solutions and wares can digitally transform any organizations, and customers latch on to these ‘shiny’ tech. End users get too fixated that technology is the core of Digital Transformation. They are wrong.

Missing the Forest

As I gather more insights through observations, and more conversations and more experiences, I think most of the “digital transformation ready” organizations are not adopting the right approach to Digital Transformation.

Digital Transformation is not tactical. It is not a one-time, big bang action that shifts from not-digitally-transformed to digitally-transformed in a moment. It is not a sprint. It is a marathon. It is a journey that will take time to mature. IDC and its Digital Transformation MaturityScape Framework is spot-on when they first released the framework years ago.

IDC Digital Transformation Maturityscape

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Is AI my friend?

I am sorry, Dave …

Let’s start this story with 2 supposed friends – Dave and Hal.

How do we become friends?

We have friends and we have enemies. We become friends when trust is established. Trust is established when there is an unsaid pact, a silent agreement that I can rely on you to keep my secrets private. I will know full well that you will protect my personal details with a strong conviction. Your decisions and your actions towards me are in my best interest, unbiased and would benefit both me and you.

I feel secure with you.

AI is my friend

When the walls of uncertainty and falsehood are broken down, we trust our friends more and more. We share deeper secrets with our friends when we believe that our privacy and safety are safeguarded and protected. We know well that we can rely on them and their decisions and actions on us are reliable and unbiased.

AI, can I count on you to protect my privacy and give me security that my personal data is not abused in the hands of the privileged few?

AI, can I rely on you to be ethical, unbiased and give me the confidence that your decisions and actions are for the benefit and the good of me, myself and I?

My AI friends (maybe)

As I have said before, I am not a skeptic. When there is plenty of relevant, unbiased data fed into the algorithms of AI, the decisions are fair. People accept these AI decisions when the degree of accuracy is very close to the Truth. The higher the accuracy, the greater the Truth. The greater the Truth, the more confident people are towards the AI system.

Here are some AI “friends” in the news:

But we have to careful here as well. Accuracy can be subjective, paradoxical and enigmatic. When ethics are violated, we terminate the friendship and we reject the “friend”. We categorically label him or her as an enemy. We constantly have to check, just like we might, once in a while, investigate on our friends too.

In Conclusion

AI, can we be friends now?

[Apology: sorry about the Cyberdyne link 😉 ]

[This blog was posted in LinkedIn on Apr 19th 2019]

Data Privacy First before AI Framework

A few days ago, I discovered that Malaysia already had plans for a National Artificial Intelligence (AI) Framework. It is led by Malaysia Digital Economy Corporation (MDEC) and it will be ready by the end of 2019. A Google search revealed a lot news and announcements, with a few dating back to 2017, but little information of the framework itself. Then again, Malaysia likes to take the “father knows best” approach, and assumes that what it is doing shouldn’t be questioned (much). I will leave this part as it is, because perhaps the details of the framework is under the OSA (Official Secrets Act).

Are we AI responsible or are we responsible for AI?

But I would like to highlight the data privacy part that is likely to figure strongly in the AI Framework, because the ethical use of AI is paramount. It will have economical, social and political impact on Malaysians, and everybody else too. I have written a few articles on LinkedIn about ethics, data privacy, data responsibility, impact of AI. You can read about them in the links below:

I may sound like a skeptic of AI. I am not. I believe AI will benefit mankind, and bring far reaching developments to the society as a whole. But we have to careful and this is my MAIN concern when I voice about AI. I continue to question the human ethics and the human biases that go into the algorithms that define AI. This has always been the crux of my gripes, my concerns, my skepticism of everything we call AI. I am not against AI but I am against the human flaws that shape the algorithms of AI.

Everything is a Sheep (or a Giraffe)

A funny story was shared with me last year. It was about Microsoft Azure computer vision algorithm in recognizing visuals in photos. Apparently the algorithm of the Microsoft Azure’s neural network was fed with some overzealous data of sheep (or giraffes), and the AI system started to point out that every spot that it “saw” was either a sheep, or any vertical long ones was a giraffe.

In the photo below, there were a bunch of sheep on a tree. Check out the tags/comments in the red rectangle published by the AI neural network software below and see how both Microsoft Azure and NeutralTalk2 “saw” in the photo. You can read more about the funny story here.

This proves my point that if you feed the learning system and the AI behind it with biased and flawed information, the result can be funny (in this case here) or disastrous. Continue reading