Tag Archives: Machine Learning

A Necessity to Optimise & Leverage The Cloud – Lessons From Carsome and 500 Startups

September 15, 2021 Nur Jeevan Leave a comment

Startups have become the norm nowadays. They’ve become a hallmark for not just the tech industry but also a thriving economy. However, when it comes down to it, the startup arena can also become one of the most brutal, unforgiving arenas any founder or individual can find themselves. The world has its eyes on Southeast Asia – Malaysia included – as its startup ecosystem teeters on the verge of another boom. The start-up arena has become one of the largest spaces for investment in the region, attracting some USD$1.48 billion in just Q1 of 2021 alone according to CB Insights. A significant chunk of 40.6% of this investment is driven by early-stage deals.

man in black crew neck t shirt sitting beside woman in gray crew neck t shirt — Photo by Canva Studio on Pexels.com

So, the big question is, what do we do with this data? We’ve heard tonnes of startup stories – so, we’re offering a slightly different perspective. Let’s talk about the tech. Yes, not every startup is an app or tech-related. However, with the rapidly changing needs and challenges now, it has become even more important for startups to be able to adapt and react accordingly – in a word – AGILE. Again, it’s a term we’ve heard or read countless times. That said, it’s become even more important now that they do – it could be the difference between survival and disappearing into the ether.

Fail Efficiently, Innovate Quickly

Like a wise woman once sang – “Let’s start at the very beginning. A very good place to start…”. The world as we know it has changed over the past few decades. In fact, it’s changed in the past few years! The costs of starting a startup have reduced from USD$5 million in 1999 to just over USD$50,000 in 2010 and continue to decline.

The biggest difference? The Cloud. Cloud computing has significantly reduced the capital needed to start-up enterprises and it will continue to do so. Companies like Amazon Web Services (AWS) are enabling agility and cost-efficiency. They are enabling startups to take off with no upfront costs but most importantly they encourage startups to experiment and fail fast – allowing them to move forward with innovating their next approach. Each failure allows startups to learn, optimise and eventually succeed.

“The great thing about startups is the ability to start small and learn as you go. So long as you get the foundations right – such as ensuring you are secure by design from the outset – it won’t matter so much if you make the odd misstep along the way, because the consequences will be small.”
Digbijoy Shukla, Business Development Lead, Startup Business Development ASEAN, AWS

Digbijoy Shukla Business Development Lead Startup Business Development ASEAN AWS

These flexibilities are key in startups as it goes without saying – the road to their success is how fast they are able to present and prove their concept. The ability to provision and decommission servers and technological resources quickly and efficiently will help these start-ups further optimise and conserve resources. With this inherent efficiency built in it falls to start-ups and their management to take advantage of the tools at their fingertips to enhance their offering, evolve their approach and embrace the insights they are privy to.

The Right Cloud Computing Partners can determine the Success of Startups

The ability to fail fast and experiment comes secondary to the tools any startup has at its disposal. Cloud computing continues to be a necessity simply because of its robust offerings. Going digital is no more about changing typewriters to desktops, it’s about a set of tools that allow you to create, adapt and react to ensure that the company is meeting its clients’ and customers’ needs.

Khailee Ng Managing Partner 500 Startups

“It’s critical to align yourself with the right partners and support as early as possible. Folks like 500 Startups and AWS aren’t here to be new and trendy, we’ve been part of the core ecosystem infrastructure since the early days.”
Khailee Ng, Managing Partner, 500 Startups

Choosing the right cloud, then, is an essential part of a start-up’s success. It’s like choosing the right business partner, you need someone who believes in your vision and complements your skills with the correct tools. With the number of Cloud providers continually increasing, start-ups are forced to make a choice based on the needs and skill level of their organisation.

In our session with AWS, Khailee Ng, Managing Partner at 500 Startups, stressed that getting the right partner can be akin to getting that first investment. Programs like AWS Activate enable startups to continue experimenting and functioning while upskilling and adapting. It creates a simultaneous process in which founders, staff and enablers are continually interacting and improving. In fact, programmes like AWS Activate essentially provide startups with an infusion of not just credits for experimentation and setting up, it provides a platform for startups to learn and implement the relevant knowledge for their success. AWS also provides technical support which allows non-technical founders to also benefit.

Scale, Pivot and React with Actionable Insights from the Cloud

Being on the Cloud is not always about cost or efficiency. It’s about the amount of data that will be available from the experimentation and even day to day usage of services and products. The data and insights that it gives will invariably determine the direction in which the startup can grow. In fact, if utilised properly, this data can even provide insights into new niches and services that can grow the startup’s user base and open new markets.

In the initial six months, we were a car listings site. We pivoted the business in 2016, based on the data. We then extended our sales online, with customer benefits such as five days money back guarantee. Our (sales) pickup rate became much stronger, as we saw the same level of sales (as what we experienced) before the lockdowns. It’s really all about navigating successfully through this crisis.”
Eric Cheng, Co-Founder and CEO of Carsome, an integrated car e-commerce platform

AWS Article Carsome Stock Photos 002 — Source: Adobe Stock

Take, for instance, Malaysian born startup – Carsome which started as a platform for searching for second-hand cars. The company ended up pivoting to complement its pre-existing service. They expanded to include the sales and purchase of these vehicles based on insights derived from the data generated by their users. They were able to gain insights that highlighted a niche that they could occupy; more importantly, it complemented their existing product. With these insights, they were quickly able to adapt, react and develop an offering that enhanced their product and led to exponential growth. They continue to use this data to enhance their service and ensure user happiness.

Of course, the Cloud doesn’t just provide for actionable insights and agility. It’s also about offloading mundane tasks and leveraging offerings like AWS Sagemaker. Implementing AI and Machine Learning in taking over tasks that can and should be automated will allow startups to focus their workforce on more pertinent tasks that will allow them to differentiate themselves further. Focusing on what is important will allow startups to eventually be able to scale. Of course, this doesn’t mean that vital tasks are offloaded, but it does mean that startups are able to maximise efficiency and optimise their workforce allowing them to flourish.

The Cloud Is Not the Future, It is Now

We keep hearing that the Cloud is the future. In truth, startups and companies that fail to adopt and adapt are bound to be held back by their own inefficiencies and stigmas. It is crucial that we realise that the Cloud is now – it’s not the future; at least, not anymore. Leveraging the Cloud and its many tools is a pivotal skill that startups need to develop. In fact, it would not be unfounded to say that it is a skill that all organisations should already be developing.

We are at a stage in the world where technology has already proliferated every aspect of our lives; from our entertainment to our work and even in our day-to-day lives. Why then are we hesitant to adopt it at scale to increase our own efficiencies and productivity? Why are we hesitant to put technology – already available – to use to increase profitability?

Startups cannot wait to adopt Cloud computing anymore. In fact, they are setting themselves up for failure without the proper Cloud and the willingness to learn how to use it. You don’t need to be a rocket scientist to put technology to work for you in this day.

Business, Editorial

Cloud, 5G, Machine Learning & Space: Digital Trends Shaping the Future

June 15, 2021 Nur Jeevan Leave a comment

The world is arguably never going to be the same after the COVID-19 pandemic. The sentiment rings true in many aspects and sectors even now, a year on. However, the effects of the pandemic have spurred our normal to take a digital shift in which more companies are accelerating their digital transformation journeys with some further than others. That said, the adoption of technologies has created waves and trends that seem to be influencing everything in our lives.

In a nutshell, these trends are going to change the way we approach a whole myriad of thing from the way we work to the way we shop. We’re seeing businesses like your regular mom and pop shops adopt cloud technologies to help spur growth while digital native businesses and companies are doing the same to adapt to the ever-changing circumstances. The adoption of technologies and, in particular, cloud technologies, is building resilience in businesses like never before.

Our interview with the Lead Technologist for the Asia Pacific Region at Amazon Web Services (AWS), Mr Olivier Klein, sheds even more light on the trends that have and continue to emerge as businesses continue to navigate the pandemic and digitisation continues.

The Cloud Will Be Everywhere

As we see more and more businesses adopt technologies, a growing number of large, medium and small businesses will turn to cloud computing to stay competitive. In fact, businesses will be adopting cloud computing not only for agility but due to increasing expectations that will come from their customers. However, when referring to “The Cloud”, we are not only talking about things like machine learning, high performance computing, IoT and artificial intelligence (AI); we’re also talking about the simple things like data analytics and using digital channels.

pexels photomix company 106344 — Photo by **PhotoMIX Company** from **Pexels**

Digitization journeys are creating expectations on businesses to be agile and adaptable. That said, businesses with humble beginnings like Malaysia’s TF Value-Mart have been able to scale thanks to their willingness to modernize and migrate to the cloud. Their adoption of cloud technologies has created a more secure digital environment for their business and has augmented their speed and scalability. This has allowed them to scale from a single, mom and pop store in Bentong in 1998 to over 37 outlets today.

The demand for cloud solution is increasing and there’s no deny it. Even businesses like AWS have had to expand to accommodate the growing demands for digital infrastructure and services. The company has scaled from 4 regions in their first 5 years to 13 regions today with more coming in the near future. AWS’s upcoming regions include six upcoming regions, of which four are in Asia Pacific: in Jakarta, Hyderabad, Osaka and Melbourne.

Edge Computing Spurred by 5G & Work From Anywhere

In fact, according to Mr Klein, AWS sees the next push in Cloud Computing coming from the ASEAN region. This will, primarily, be spurred by the region’s adoption of 5G technologies. Countries like Japan and Singapore are already leading the way with Malaysia and other countries close behind. The emergence of 5G technologies is creating a new demand for technologies that allow businesses to have a more hybrid approach to their utilisation of Cloud technologies.

As companies continue to scale and innovate, a growing demand is emerging for lower latencies. While 5G allows low latency connections, some are beginning to require access to scalable cloud technologies on premises. Data security and low latency computing are primary drives behind this demand. Businesses are innovating faster than ever before and require some of their workloads to happen quicker with faster results. As a result, we see a growing need for services like AWS Outpost which allows businesses to bring cloud services on premises, and with their recent announcement at AWS re:Invent, Outposts are becoming even more accessible.

Edge computing is also part and parcel of cloud computing as the mode in which we work continues to change. With most businesses forced to work remotely during the pandemic, the trend seems to be sticking; companies are beginning to adopt a work from anywhere policy which allows for more employee flexibility and increased productivity. That said, not all workloads are able to follow where workers go. With the adoption of 5G, that is no longer the case. Businesses will be able to adopt services like AWS Wavelength to enable low latency connection to cloud services empowering the work from anywhere policies.

The same rings true when it comes to education. The growth experienced in the adoption of remote learning will continue. Services like Zoom and Blue Jeans have become integral tools for educators to reach their students and will continue to see their roles expand as educational institutions continue to see the increased importance of remote learning.

Machine Learning is The Way

As edge computing and Cloud become the norm, so too will machine learning. Machine learning is enabling companies to adopt new approaches and adapt to changing circumstances. The adoption of machine learning solutions has paved the way to new expectations from customers that has and will continue to spur its adoption. In fact, Mr Klein, tells us that businesses will not only be adopting machine learning for automation but also to provide better customer experiences. What’s more, a growing number of their customers are also going to expect it.

Machine Learning’s prevalence is going to grow in the coming years – that’s a given. Customers and users have already had their experiences augmented by AI and machine learning. This has and continues to create expectations on how user experiences should be. Take for instance, services like Netflix have been using machine learning and AI to recommend and surface content to their users. Newer streaming services which lack these integrations are seen to be subpar and are criticised by users.

lenny kuhne jHZ70nRk7Ns unsplash — Photo by Lenny Kuhne on Unsplash

Aside from user experiences, businesses are getting more accustomed to using machine learning to provide insights when it comes to making decision making and automating business operations. It has also enabled companies to innovate more readily. These conveniences will also be one of the largest factors in the increasing prevalence. It will also see increased adoption which will be largely attributed to the adoption and development of autonomous vehicles and other augmented solutions.

Companies like Moderna have been utilising machine learning to help create and innovate in their arena. They have benefitted from adopting machine learning in their labs and manufacturing processes. This has also allowed them to develop their mRNA vaccines which are currently being deployed to combat COVID-19.

To Infinity & Beyond

The growing adoption of digital and cloud solutions is also spurring a new wave of technologies which allow businesses deeper insights. These technologies allow businesses to access insights gained from satellite imaging. Data such as ground imaging and even ocean imaging can be used to gain actionable insights for businesses. Use cases are beginning to emerge from business involved in logistics, search and rescue and even retail.

However, the cost of building and putting a satellite in orbit is nonsensical for a business. That said, we already have thousands of them in orbit and it would make more sense to use them to help gain these insights. AWS is already introducing AWS Ground Station – a fully managed serve that gives businesses access to satellites to collect and downlink data which can then be processes in AWS Cloud.

These trends are simply a glance into an increasingly digitised and connected world where possibilities seem to be endless. Businesses are at the cusp of an age that will see them flourish if they are agile and willing to adopt new technologies and approaches that are, at this time, novel and unexplored.

Business, Health

Acer Expands to Healthcare with a Focus on AI-Assisted Diagnostics

May 28, 2021 Nur Jeevan Leave a comment

Acer has been really busy in the recent past expanding its portfolio to become a more well-rounded tech and lifestyle company. In recent years, the company has already introduced the Predator Shot, an energy drink targeted at gamers, the Predator Gaming Chair, a collaborative effort with OSIM, and even a brand new brand – Acerpure. The company isn’t just stopping there though. It looks like they are expanding into the healthcare segment and it’s happening really soon.

In an interview session with the media, President of Acer Pan Asia Pacific Operations, Mr Andrew Hou, unwittingly revealed that the company would be exploring opportunities in healthcare in the near future. Upon further investigation, we found that Acer has already set up a new subsidiary, Acer Healthcare. The company is listed in the Tracxn database and is noted to be founded in 2019. Acer has also set up an official website for Acer Healthcare.

Source: Channel News Asia / Mr Andrew Hou, President of Acer Pan Pacific Operations

It looks like Acer is looking to leverage its prowess in dealing with data and technology to help bridge the closing gap between technology and medicine. Acer Healthcare seems to be looking into using AI-powered devices to help with diagnosis and patient monitoring. The field has been growing in the past few years with multiple startups and companies exploring opportunities and new technologies that can help better diagnose patients.

Acer Healthcare has already released a product called VeriSee DR, an AI-assisted solution for diagnosing Diabetic Retinopathy – a condition that affects close to 130 million people worldwide. Using Acer’s VeriSee DR, the condition can be diagnosed by utilising AI to analyse pictures of patients’ ocular fundus (the interior of the eye) for signs of diabetic retinopathy. According to their website, the technology works with a 95% sensitivity with 90% specificity for diagnosis. In fact, Acer Healthcare has ongoing clinical trials with the VeriSee DR and has published research on it in multiple medical journals.

anonymous oculist examining vision of patient on eye screener — Photo by Ksenia Chernaya on Pexels.com

In addition to VeriSee DR, it looks like Acer Healthcare is focusing on research and development of new diagnostic technologies using AI. Of note are a few currently listed research projects which include the diagnosis of heart arrhythmia using AI analysis of data collected from continual detection using an Acer Leap Ware wearable device and the diagnosis of renal impairment through retinal fundus imaging. While it does seem like the company’s focus is on diagnostic technologies they are also working on technologies for medical record and referrals as well.

Editorial, Health

The Future of Health Lies in Technology But We’re Not Ready According to the Philips Future Health Index

February 9, 2021 Nur Jeevan Leave a comment

It goes without saying that technology is seeping into every aspect of our lives. This was a theme that Philips found to be true even when it comes to the medical field. In fact, technology is becoming so ubiquitous that the Future Health Index (FHI) has indicated that in a broad sense, the field of medicine simply isn’t ready. Their yearly survey of younger medical professionals had very interesting findings this round given that it was commissioned in the early months of the COVID-19 pandemic.

Younger Doctors Want Technology – It is the Key to value-based healthcare

In its fifth year, the Future Health Index found, among other things, that younger doctors are open to adopting technologies to assist in the mundane, repetitive tasks of medicine. In fact, nearly one in three doctors saw benefits in adopting technologies such as artificial intelligence, automation and telehealth in the day to day functions of medicine. 76% of doctors also cited that the adoption of technologies was able to help with decreasing the stresses of medical practice – one of the main worries with frontliners in the current pandemic.

However, the findings of Philip’s FHI show that key competencies which are key to a digital healthcare system are lacking in basic medical training – of interest is the lack of data competencies among younger medical professionals. In the FHI, about 47% of respondents found that they were left in the lurches when it came to key data competencies. Skillsets such as data analysis and interpretation were among the skills that were cited. Another notable competency when it came to data, was the management of data privacy, one of the current growing concerns of society.

These particular findings highlight a robust issue that should be tackled in academia as well as with continuing medical education. Only 54% of doctors in Asia Pacific reported receiving training to address the legislative issues pertaining to data privacy while only 51% were receiving training in handling data.

These competencies are key in the current shift towards value-based healthcare. A healthcare model that measures patient outcomes as a key factor in determining the value of healthcare. While there is a good awareness of the term in the Asia Pacific region (82%), drilling further found that an alarming 4% knew what it was entirely. The majority of doctors surveyed only knew it by name.

While that may be a concern, the integration of technology into everyday healthcare and patient care is key in a value-based system. Only when doctors can access, interpret and analyse the data coming from adopted technologies, can they truly access the quality of healthcare. Key appreciations of technology in reducing their mundane workloads need to be more pervasive.

Technology in Improving Healthcare

Technology plays a vital role in creating a more efficient and effective standard of health. In their FHI, Philips found that a majority of younger doctors are advocates of adopting newer technologies. They see value in adopting the right technologies in creating a better standard of care.

However, in countries like Malaysia, these doctors are facing issues even with the simplest issue of automation of administrative tasks. That said, medical practice is being revolutionised by technologies that were once farfetched are becoming a reality. As the issue of personalised healthcare comes to the forefront, we have an increasing amount of doctors across the Asia Pacific region who see the benefits of having Artificial intelligence applied in the field. 74% surveyed opportunities to offer more personalised care while 79% believed that AI would help with more accurate diagnoses.

Photo by National Cancer Institute on Unsplash

That said, for AI to be effective, data needs to be made readily available. Nevertheless, the medical industry faces a data conundrum – should more effective and personalized healthcare come at the expense of data privacy? That said, the conundrum is addressed by anonymizing patient data to allow ready access. However, with the multiple data silos created by multiple software platforms, doctors are strained to have any actionable insights.

Interoperability is becoming a hurdle as hospitals and even clinics begin adopting new technologies that are not speaking to each other. This lack of interoperability creates data silos which doctors have to manually import and analyse. With a more cohesive digital architecture, doctors will be able to access a more holistic view of patient data and outcomes; and with the state of AI and machine learning now, they will be able to get even more insights to tough cases.

Technology isn’t just for the betterment of patient care, the FHI has also found that younger doctors report being less stressed at work when technologies are adopted effectively. The psychological benefits of reduced stress on the doctors will undoubtedly benefit patient care in the long run.

Looking to the Future & What the Medical Field can Learn from the Digitisation of Other Industries

Younger doctors are the key to the field of medicine progressing into the future. When it comes to their willingness to learn, it comes as no surprise that these doctors are spearheading the charge to adopt and learn new skills to remedy the skills gap that is emerging. However, it now falls to academia to address the needs in the nascent class of doctors emerging from their institutions into a field of medical practice that is both familiar and different.

What remains is for the medical industry to look to others who have a head start in dealing with the issues they are facing now. New technologies being adopted such as Kubernetes and the cloud could see the medical industry getting a quantum leap when it comes to patient care and medical breakthroughs.

There is no better proof of the benefits of adopting the right technology than the state of vaccines for COVID-19. In a matter of months, multiple vaccine candidates have been developed. Some candidates such as the mRNA vaccine are revolutionary approaches which were made possible with the augmentation of human ingenuity with the insights derived from machine learning and AI.

In addition to technologies, their adoption needs a fundamental change in attitudes and values in the industry as well. Younger Doctors are already aware of these attitudes with an increasing number looking to autonomy in their practices. They also look to workspaces which are collaborative and have access to the latest medical equipment. However, more importantly, they look to a culture that supports work-life balance.

As with any industry, a majority of the attitudes will need a top-down approach; spearheaded by veteran doctors and administrators in hospitals and practices. It goes without saying that the agility needed to adapt and adopt new technologies and approaches must be spearheaded by leadership. They will also need to look into empowering younger doctors to be bold in their approaches and use of new technologies.

Computers, Contributed, Editorial

We’re in the Golden Age of Machine Learning, Tomorrow it will be Ubiquitous – Four Things We Need to Do Now

February 4, 2021 Swami Sivasubramanian Leave a comment

Today, thanks in large part to the cloud, actions such as communicating over text or transferring funds digitally are so commonplace, we hardly even think about how incredible these processes are; as we enter the golden age of machine learning, we can expect a similar boom of benefits that previously seemed impossible.

Machine learning is already helping companies make better and faster decisions. In healthcare, the use of predictive models created with machine learning is accelerating research and discovery of new drugs and treatment regiments. In other industries, it’s helping remote villages of Southeast Africa gain access to financial services, and matching individuals experiencing homelessness with housing.

While the short term applications are encouraging, machine learning could potentially have an even greater impact on our society. In the future, machine learning will be intertwined and under the hood of almost every application, business process, and end-user experience. However, before this technology becomes so ubiquitous that it’s almost boring, there are four key barriers to adoption we need to clear first.

Democratizing machine learning

The only way that machine learning will truly scale is if we as an industry make it easier for everyone – regardless of their skill level or resources – to be able to incorporate this sophisticated technology into applications and business processes.

green and white lights — Photo by cottonbro on Pexels.com

To achieve this, companies should take advantage of tools that have intelligence directly built into applications that their entire organization can benefit from. For instance, 123RF, a homegrown stock photography portal, aims to make design smarter, faster, and easier for users. To do so, it relies on Amazon Athena, Amazon Kinesis, and AWS Lambda for data pipeline processing. Its newer product Designs.ai Videomaker uses Amazon Polly to create voice-overs in more than 10 different languages. With AWS, 123RF has maintained flexibility in scaling its infrastructure and shortened product development cycles and is looking to incorporate other services to support its machine learning & AI research.

As processes go from being manual to automatic, workers are free to innovate and invent, and companies are empowered to be proactive instead of reactive. And as this technology becomes more intuitive and accessible, it can be applied to nearly every problem imaginable–from the toughest challenges in the IT department, to the biggest environmental issues in the world.

Upskilling workers

According to the World Economic Forum, the growth of AI could create 58 million net new jobs in the next few years. However, research suggests that there are currently only 300,000 AI engineers worldwide, and AI-related job postings are three times that of job searches with a widening divergence. Given this significant gap, organizations need to recognize that they simply aren’t going to be able to hire all the data scientists they need as they continue to implement machine learning into their work. Moreover, this pace of innovation will open doors and ultimately create jobs we can’t even begin to imagine today.

That’s why companies in the region like Asia Pacific University, DBS, Halodoc and others are finding innovative ways to encourage and nurture more young talents to gain new machine learning skills in fun, interactive hands-on ways, such as the AWS DeepRacer League. It’s critical that organizations should not only direct their efforts towards training the workforce they have with machine learning skills, but also invest in training programs that develop these important skills in the workforce of tomorrow.

Instilling trust in products

With anything new, often people are of two minds – either an emerging technology is a panacea and global savior, or it is a destructive force with cataclysmic tendencies. The reality is more often than not, a nuance somewhere in the middle. These disparate perspectives can be reconciled with information, transparency, and trust.

As a first step, leaders in the industry need to help companies and communities learn about machine learning, how it works, where it can be applied, ways to use it responsibly, and understand what it is not.

Second, in order to gain faith in machine learning products, they need to be built by diverse groups of people across gender, race, age, national origin, sexual orientation, disability, culture, and education. We will all benefit from individuals who bring varying backgrounds, ideas, and points of view to inventing new machine learning products.

Third, machine learning services should be rigorously tested, measuring accuracy against third party benchmarks. Benchmarks should be established by academia, as well as governments, and be applied to any machine learning-based service, creating a rubric for reliable results, as well as contextualizing results for use cases.

Regulation of machine learning

Finally, as a society, we need to agree on what parameters should be put in place governing how and when machine learning can be used. With any new technology, there has to be a balance in protecting civil rights while also allowing for continued innovation and practical application of the technology.

small judge gavel placed on table near folders — Photo by Sora Shimazaki on Pexels.com

Any organization working with machine learning technology should be engaging customers, researchers, academics, and others to best determine the benefits of its machine learning technology with the potential risks. And they should be in active conversation with policymakers, supporting legislation, and creating their own guidelines for the responsible use of machine learning technology. Transparency, open dialogue, and constant evaluation must always be prioritized to ensure that machine learning is applied appropriately and is continuously enhanced.

What’s next

Through machine learning we’ve already accomplished so much, and yet, it’s still day one (and we haven’t even had a cup coffee yet!). If we’re using machine learning to help endangered orangutans, just imagine how it could be used to help save and preserve our oceans and marine life. If we’re using this technology to create digital snapshots of the planet’s forests in real-time, imagine how it could be used to predict and prevent forest fires. If machine learning can be used to help connect small-holder farmers to the people and resources they need to achieve their economic potential, imagine how it could help end world hunger.

To achieve this reality, we as an industry, have a lot of work ahead of us. I’m incredibly optimistic that machine learning will help us solve some of the world’s toughest challenges and create amazing end-user experiences we’ve never even dreamt. Before we know it, machine learning will be as familiar as reaching for our phones.

Business, News

Maxis Becomes First Malaysian Telco Accredited as AWS Advanced Consulting Partner

December 21, 2020 Nur Jeevan Leave a comment

Maxis is one of the only telecommunications companies in Malaysia already embracing the cloud. The company embarked on its journey to become a one-stop provider for connectivity and infrastructure for Malaysia back in 2019 with an early partnership with AWS (Amazon Web Services) who is currently the most prolific web service platform in the world. Today, they are announcing that they have successfully achieved new accreditation as an AWS Advanced Consulting Partner; making them the only telecommunications company in Malaysia to have done so. This solidifies their claim to being one of the most equipped converged solutions providers in the country.

The new accreditation certifies that Maxis is equipped to provide its customers and partners with the technical support and know-how to migrate and sustain their businesses in the cloud. To achieve this, Maxis has to demonstrate a sustained competency in their workforce equipped and certified by AWS for the many services that their platform provides. This includes taking advantage of the Machine Learning and Artificial Intelligence components available on AWS.

In addition to this, Maxis is now also offering AWS Direct Connect. AWS Direct Connect allows customers to access AWS directly via a dedicated network connection with one of the many AWS Connect locations using industry-standard 802.1q VLANs. This also allows customers to partition the connection into multiple virtual interfaces easing access to object instances in the AWS public and private clouds while maintaining network separation.

The new accreditation comes on the heels of Maxis having announced key acquisitions that have bolstered the company’s position as one of the most equipped telecommunications companies in Malaysia able to empower businesses in their digitization journey. The company has also been certified in the AWS Public Sector Partner program with over 300 Maxis employees being accredited and undergone comprehensive training by AWS.

Health, News

MIT Researchers Develop AI Model that Accurately Identifies Asymptomatic COVID-19 Carriers

November 3, 2020 Nur Jeevan Leave a comment

The COVID-19 Pandemic doesn’t seem to be going away anytime soon. The virus continues to spread drastically and have a devastating effect in areas where outbreaks have occurred. However, since the early days of the pandemic, there have been reports of asymptomatic carriers; these carriers are able to spread the virus without showing any outwardly recognisable signs of infection. This also makes them one of the largest unsolved problems of the current COVID-19 pandemic. This group of individuals are less likely to seek testing and, in turn, be diagnosed and treated.

However, that’s about to change. A group of researchers at the Massachusetts Institute of Technology (MIT) have developed an AI model that has been able to accurately identify asymptomatic carriers based on the way they cough. The AI model has been able to accurately discern and identify 98.5% of coughs from confirmed COVID-19 patients and 100% of asymptomatic carriers.

Using A.I. to Identify Unique Markers in Coughs

The team at MIT, consisting of Jodi Laguarta, Ferran Hueto, and Brian Subirana, developed the mode on a neural network called ResNet50. ResNet50 is a type of neural network that is able to discern and identify differences and similarities in data. Until now, ResNet50 was used primarily in visual discernment. However, the team at MIT has applied it in identifying markers when people cough.

man in white crew neck t shirt — Photo by cottonbro on Pexels.com

Their model was initially developed to help detect early signs of Alzheimer’s which can present in the way people cough. This include the person’s emotional state, changes in lung and respiratory performance and vocal chord strength. These are known markers for someone who could be experiencing early onset Alzheimer’s.

Using the three criteria, three independent machine learning algorithms were train and then layered on each other. The team also included an algorithm for muscular degeneration on top of the model. In tandem, these machine learning layers made it possible for the team to detect and identify samples from Alzheimer’s patients.

Detecting the Indiscernible

In April, the team looked into applying the AI model to help identify COVID-19 patients. To do this, they established a website where people could record a series of coughs with their mobile phone or any other web enabled device. In addition to their submissions, participants had to fill up a survey of their symptoms, COVID-19 status, and if their method of diagnosis. Other factors such as their native language, geographical location and gender were also collected. They have, to date, collected over 70,000 recordings which amounts to about 200,000 forced cough samples. This is the largest known cough dataset that has been collected so far according to Brian Subirana.

The model proves a long known fact that COVID-19 does in fact affect respiratory function. However, it also draws similarities between the presentation of temporary respiratory degeneration to the neurodegeneration present in Alzheimer’s patients. That said, it also shows that there are sub-clinical presentations of the disease in asymptomatic individuals. The AI algorithm is able to detect and identify individuals with these presentations, providing a much needed boost to potential diagnoses of these individuals.

More significantly, the team has developed a method in which pre-screening can be done to help curb the spread of COVID-19. What’s more, their research could be the foundation of future diagnosis when it comes to sub-clinical presentations of diseases. That said, Brian Subirana highlights that the strengths of the tool lies in its ability to differentiate coughs from asymptomatic carriers from healthy individuals. He also stresses that it is not meant to be used as a definitive test for COVID-19.

eSports, Gaming, News

[next@Acer] SigridWave Bridges the Language Barrier in eSports

October 21, 2020 Jimmy Chua Leave a comment

Acer’s Planet 9 was launched a year ago as the company’s comitment to the growing eSports scene. The platform allows aspiring professional gamers to hone their skills and collaborate. The vision for this next gen platform is to provide a “training arena” where pros, semipros and enthusiasts can improve their game.

“Planet 9 is a community-oriented platform designed to give gamers everywhere a chance to interact and learn from each other. It is intended to be a social platform that caters to multiple audiences: those looking to improve are introduced to similarly skilled teammates and opponents, likewise, those just looking to chat and enjoy themselves can meet other casual players…”
Andrew Chuang, AVP, Esports Services, IT Products Business, Acer Inc.

Planet9 was designed to bring different eSports communities together in one place, and a major part of the platform is effectively managing and integrating these communities. The platform helps users to find teammates based on a variety of factors such as game type, skill level and time zone. It also gathers and records a wide variety of data such as score, pathing, kill-death ratio and death location. This provides coaches and managers information they can use to help guide their players.

This year, Acer is bringing cutting edge AI to Planet9, its next-generation eSports platform, in the form of the SigridWave In-Game Live AI Translator. SigridWave has been specially designed to handle gaming terminology and jargon. It leverages deep learning technologies to bridge language barriers allowing gamers to communicate no matter where they are from. This is an important step in enhancing the gaming experience.

When SigridWave is deplyed, it will utilise Automatic Speech Recognition (ASR) technology to recognize speech from gamers. It then converts this into strings of text, similar to how smartphones do when you use virtual assistants. This string of text is then recognised using Neural Machine Translation (NMT) technology. The NMT deployed by SigridWave has so far been trained to recognise over 10 million bilingual sentence pairs. This allows it to recognise game specific language and jargon such as “ADS” or “camping”, giving it context awareness. In-game overlays will be supported for League of Legends on launch in late 2020 or early 2021, and support will be made available for additional titles in the future.

The new technology has the potential to take competitive and professional gaming to a whole new level. Together with SigridWave, Acer also unveiled Clubs and Tournaments; two new features that will help players collaborate and compete regularly to up their game. These join a slew of new features designed to enhance competitive play and facilitate communication between brands and players.

Business, Contributed

Four Steps to Accelerate Your Machine Learning Journey

October 20, 2020 Swami Sivasubramanian Leave a comment

This is the golden age of machine learning (ML). Once considered peripheral, ML technology is becoming a core part of businesses around the world, regardless of the industry. By 2021, the International Data Corporation (IDC) estimates that spending on artificial intelligence (AI) and other cognitive technologies will exceed $50 billion.

Locally, 25% of organizations say they are setting aside at least 10% of their budget for technology, which includes investments in big data analytics (64%), cloud computing (57%), Machine Learning and artificial intelligence (33%), and robotic process automation (27%), based on the Malaysian Institute of Accountants’ “MIA-ACCA Business Outlook Report 2020″. [1] As more companies gain awareness of the importance of ML, they should work towards getting it in motion as quickly and effectively as possible.

person using a laptop — Photo by fauxels on Pexels.com

At Amazon, we have been on our own ML journey for more than two decades – applying it to areas like personalization, supply chain management, and forecasting systems for our fulfillment process. Today, there is not a single business function at Amazon that is not made better through machine learning.

Whether your company is just getting started or in the middle of your first implementation, here are the four steps you should take to have a successful machine learning journey.

Get Your Data in Order

When it comes to adopting machine learning, data is often cited as the number one challenge. We found that more than 50% of time spent in building ML models can be spent in data wrangling, data cleanup, and pre-processing stages. Therefore, prioritize investing in the establishment of a strong data strategy to avoid spending excessive time and resources on data cleanup and management.

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When starting out, the three most important questions to ask are:

What data is available today?
What data can be made available?
A year from now, what data will we wish we had started collecting today?

In order to determine what data is available today, you will need to overcome data hugging – the tendency for teams to gatekeep data they work with most closely. Breaking down silos between teams for a more expansive view of the data landscape while still maintaining data governance is crucial for long-term success.

Additionally, identify what data actually matters as part of your machine learning approach. Think about best ways to store data and invest early in the data processing tools for de-identification and/or anonymization, if needed.

Identify the Right Business Problems

When evaluating what and how to apply ML, focus on assessing the problem across three dimensions: data readiness, business impact, and machine learning applicability.

Balancing speed with business value is key. Instead of trying to embark on a three-year ML project, focus on a handful of critical business use cases that could be solved in the upcoming six to 10 months. Start by identifying places where you already have a lot of untapped data and evaluate if machine learning brings benefits. Avoid picking a problem that is flashy but has unclear business value, as it will end up becoming a one-off experiment.

Champion a Culture of Machine Learning

In order to scale, you need to champion a culture of machine learning. At its core, ML is experimentation. Therefore, it is imperative that your organization embrace failures and take a long-term view of what is possible.

high angle photo of robot — Photo by Alex Knight on Pexels.com

Businesses also need to combine a blend of technical and domain experts to work backward from the customer problem. Assembling the right group of people also helps eliminate the cultural barrier to adoption with a quicker buy-in from the business.

Similarly, leaders should constantly find ways to simplify the process of ML adoption for their developers. Since building ML infrastructures at scale is a time and labor-intensive process, leaders should encourage their teams to use tools that cover the entire ML workflow to build, train, and deploy these models efficiently.

For instance, 123RF, a homegrown stock photography portal, aims to make design smarter, faster, and easier for users. To do so, it relies on Amazon Athena, Amazon Kinesis, and AWS Lambda for data pipeline processing. Its newer products like Designs.ai Videomaker uses Amazon Polly to create voice-overs in more than 10 different languages. With AWS, 123RF has maintained flexibility in scaling its infrastructure and shortened product development cycles and is looking to incorporate other services to support its machine learning & AI research.

Develop Your Team

Developing your team is essential to foster a successful machine learning culture. Rather than spending resources to recruit new talent in a competitive market, hone in on developing your company’s internal talent through robust training programs.

group of people sitting indoors — Photo by fauxels on Pexels.com

Years ago, Amazon created an in-house Machine Learning University (MLU) to help its own developers sharpen their ML skills or equip neophytes with tools to get started. We made the same machine learning courses available to all developers through AWS’s Training and Certification offering.

DBS Bank, a Singaporean multinational bank, employed a different approach. It is collaborating with AWS to train its employees to program their own ML-powered AWS DeepRacer autonomous 1/18^th scale car, and race among themselves at the DBS x AWS DeepRacer League. Through this initiative, it aims to train at least 3,000 employees to be conversant in AI and ML by year end.

[1] MIA (Malaysian Institute of Accountants) and ACCA (Association of Chartered Certified Accountants), Business Outlook Report 2020, 2020

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