Singapore’s war for talent remains fierce despite a more upbeat outlook on the economy, with employers struggling to find suitable candidates to fill positions. 

Employers in Singapore are forced to rethink their recruitment practices as job vacancies are at a three-year high. 

According to Jobstreet’s 2021 Job Report, the Computer / IT industry has seen the hiring appetite return to pre-pandemic levels despite a 20.3% dip in 2020 — with this number expected to continue growing. 

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Jamilah Lim | 1 November, 2021

Singapore’s war for talent shows more roles require specific specialist expertise

Employers look for fresh talent to fill their open positions as the economy improves. This is to keep up with the rapid changes in technology and the fast-evolving workplace. However, they face two big problems: The job market is flooded with CVs, and most potential candidates do not have the experience or skills required. 

Highly skilled IT professionals are sought-after across all industries with the imminent growth of the information technology industry and rapid adoption of digitalization. 

This highlights the importance of promoting reskilling and upskilling among Singapore’s workforce to meet changing job landscape needs and as more roles require specific specialist expertise.

Tech Talent Expectations

The 2021 Tech Talent Expectation Survey, commissioned by Randstad Singapore and independently conducted by YouGov, showed that only 48% of ICT respondents receive job interview requests at least once a month despite the high demand for tech talent in Singapore.

It also showed that IT candidates are incredibly selective about the types of companies and jobs that they want to work in, especially if they have in-demand skills and plenty of options to choose from. 

Decisive motivating factors for active job seekers include working for tech companies at the forefront of innovation and abundant opportunities for innovative trial technologies. Young tech workers are the most likely to accept job interviews, with 29% of them accepting interview requests 75% to 99% of the time.

Singapore’s technology sector is bracing itself for an exodus of talents as young professionals turn down job offers despite attractive compensation packages and benefits.

One of the reasons for these rejected offers is that the interviewer did not understand the job scope or role they were hiring for. Meanwhile, an overwhelming 96% want to switch their specialisation if given a choice. 

Singapore tech skills gap

In the coming years, tech firms will require a range of skills to promote Singapore and build innovative technology platforms. They will need talent who have experience with emerging technologies like artificial intelligence, machine learning and professionals with skills in data science and cybersecurity. 

According to a new report commissioned by Amazon Web Services (AWS), Singapore will need 1.2 million additional digital workers by 2025 to remain competitive. 

However, Singapore’s skills in the ICT sector are lacking in “cybersecurity” as the top specialisation followed by “artificial technology, automation and robotics” and “data science/analytics”.

While technology experts, business leaders and policymakers have identified cloud architecture design, cybersecurity, software operations support, web/software/game development, and large-scale data modelling as among Singapore’s top five in-demand skills by 2025.

The post Singapore’s war for talent remains fierce appeared first on Tech Wire Asia.

In 2020 alone, some 1.7mb of data was created every second by every person. To top it off, a whopping 90% of the world’s data was created in the last two years alone. In short, energy use by data centers and IT will only continue to rise–ultimately speeding up the development of green data centers.

Vertiv, a global provider of digital infrastructure and continuity solutions, in its recently released annual list of the key data center trends to watch in 2022, also emphasized on a dramatic acceleration in actions to address sustainability and navigate the climate crisis. “As we move into 2022, data center operators and suppliers will actively pursue strategies that can make a real difference in addressing the climate crisis,” Vertiv CEO Rob Johnson said.

Now, as we near the new year, we look at possible data center trends we may see in 2022 and beyond, as highlighted by experts.

Green data centers: tackling sustainability and the climate crisis

According to Vertiv, the data center industry has taken steps toward more climate-friendly practices in recent years, but operators will join the climate effort more purposefully in 2022. On the operational front, Vertiv experts predict some organizations will embrace sustainable energy strategies that utilize a digital solution that matches energy use with 100% renewable energy and ultimately operates on 24/7 sustainable energy. 

“Such hybrid distributed energy systems can provide both AC and DC power, which adds options to improve efficiencies and eventually allows data centers to operate carbon-free. Fuel cells, renewable assets, and long-duration energy storage systems, including battery energy storage systems (BESS) and lithium-ion batteries*,all will play a vital role in providing sustainable, resilient, and reliable outcomes,” the report added. 

On lithium-ion batteries, Vertiv experts expect the lithium battery recycling infrastructure to expand in 2022 and eliminate one of the few remaining barriers to widespread adoption of lithium-ion batteries in the data center. In a more immediate term, extreme weather events related to climate change will influence decisions around where and how to build new data centers and telecommunications networks, Vertic predicts. 

“Other factors, including the reliability and affordability of the grid, regional temperatures, availability of water and renewable and locally generated sustainable energy, and regulations that ration utility power and limit the amount of power afforded to data centers, play a part in the decision-making as well,” it added.

Artificial Intelligences in data centers gets real

The need for real-time computing and decision-making are becoming more critical, given how today’s networks get more complex and more distributed. To top it off, the augmented and virtual reality demands of the metaverse too are becoming more prominent. 

“This real-time need is sensitive to latencies, and under the increasingly common hybrid model of enterprise, public and private clouds, colocation, and edge, full-time manual management is impractical, if not impossible,” the report states.

That said, artificial intelligence (AI) and machine learning (ML) will be critical to optimizing the performance of these networks, Vertiv said. “It will take focus and time to collect the right data, build the right models, and train the network platform to make the right decisions. 

Even smaller companies are embracing AI, according to the report, given the availability of AI hardware from established vendors, cloud options for the same, a simplified toolchain, and an educational focus on data science. “It all adds up to accelerated AI adoption in 2022,” Vertiv noted.

The post-pandemic data center will take stage

According to Vertiv’s data, some 2.9 gigawatts worth of new data center construction is underway globally. “Those data centers will be the first built specifically to meet the needs of a post-COVID world. More activity will be focused at the edge, where VMware projects a dramatic shift in workload distribution – from 5% currently to 30% over the next five years,” it added.

While availability will remain the top priority, even at the edge, Vertiv noted that lower latency is a rising need to support healthy buildings, smart cities, distributed energy resources, and 5G. “[Overall]2022 will see increased investment in the edge to support this new normal (remote work, increased reliance on ecommerce and telehealth, video streaming) and the continuing rollout of 5G,” it concluded.

The post 2022: Green is the color of data centers appeared first on Tech Wire Asia.

AI is so pervasive in China, that it’s used in everything from online shopping to… let’s just call it Big Brother activities.

Now, Chinese scientists have developed an AI “prosecutor” that can charge people with crimes. It was developed by a team led by Professor Shi Yong, director of the Chinese Academy of Sciences’ big data and knowledge management laboratory. 

Professor Shi claims the machine is able to file a charge with a whopping 97% accuracy based on a verbal description of the case.

Theoretically, the machine would be able to reduce the workloads of prosecutors, so they can focus their time and efforts on more difficult tasks. 

“The system can replace prosecutors in the decision-making process to a certain extent,” said Shi and his colleagues in a paper published this December in the domestic peer-reviewed journal Management Review.

We know it sounds like an android judge fitted with a wig and robes will be banging a gavel, calling silence in the courtroom, but that’s not really how it works — it’s really just an AI machine on a desktop computer, processing cases.

Not the first time China has used AI in the judiciary

Despite the aplomb with which the news broke, this isn’t actually China’s first foray into using AI in legislation. AI was introduced into the court process as early as 2016, through a tool known as System 206, according to SCMP.

System 206 can evaluate the strength of evidence, conditions for arrests, and the level of a suspect’s danger to society.

Nevertheless, the limitations of existing AI tools such as System 206 were that they were not designed to be a part of the decision-making process of filing charges and suggesting sentences, according to Shi.

Such higher-level decision-making requires the AI machine to identify and sort details of a case file and remove data that are extraneous or irrelevant to the crime whilst still keeping pertinent information. 

Furthermore, it would need to ‘convert complex, ever-changing human language into a standard mathematical or geometric format that a computer could understand.”

According to SCMP, charges can be meted out to suspects based on 1,000 traits (or variables) pulled from the human-generated case description text. The evidence would then be left to System 206 for assessment.

The machine was fed with over 17,000 cases from between 2015 and 2020 in order for it to learn how to recognize, sort, and include or exclude pertinent information.

It is so far able to prosecute eight of the most common crimes with a 97% accuracy. They include credit card fraud, illegal gambling operations, reckless driving, intentional injury, obstruction of official duties, theft, and fraud.

In typical China fashion, “picking quarrels and provoking trouble” are also criminal offenses — which the AI is able to recognize too… obviously. 

Shi and colleagues expect the AI prosecutor to, over time and with improvements, increase in accuracy and scope of function. Examples include recognizing uncommon crimes and filing multiple charges against a single suspect.

China not the first to use AI in sentencing

This is not the first instance of the use of AI in the judiciary system. 

In February 2020, Malaysia made history as its judiciary was the first to use AI in sentencing. 

Local reports said the AI would analyze a database of cases between 2014 and 2019 in the Eastern states of Sabah and Sarawak prior to recommending actions to the court.

Currently, the AI system the in East Malaysian judiciary is used for crimes such as drug possession and rape. 

The danger of AI biases

Importantly, when it comes to machine learning, AI bias plays a massive role in determining the outcome of things. Feed the machine with the wrong kind of information, and you’d get screwed-up results that can maim, kill or put the wrong people behind bars for life. 

AI bias can be so pervasive, silent, and invisible — many do not even notice that it exists in not just the information fed to the machine, but also how the entire machine is designed, and who designs it. 

Human beings by default, are already biased to begin with — especially when bias is deeply entrenched systemically in societies.

This makes engineering a bias-free machine learning system that doesn’t cause destruction to lives rather difficult.

Tech companies are quickly realizing this, and some have even embarked on programs to weed out AI biases, such as Twitter.

We’ve already seen how AI bias has caused deaths from autonomous cars, affected healthcare provision on the basis of race, and also discriminated against female job applicants, among a litany of other problematic issues. 

In Wisconsin, an AI risk assessment software called COMPAS was used in sentencing. The AI in COMPAS estimates the likelihood of criminals re-offending based on their responses to 137 survey questions. 

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However, a study found discrimination in how it assessed criminals based on their ethnicity. 

Black criminals were often labeled as higher-risk re-offenders even when they do not re-offend. 

Conversely, it produced the opposite results for white criminals by labeling them as lower-risk re-offenders even when they re-offend. 

There still remain important questions when it comes to its use in cases impacting actual human lives — AI bias is one, but ultimately, there is the question of who eventually takes responsibility.

In the case of China, will it be the prosecutors, AI machine, or the algorithm designer(s)?

The post China has developed an… AI prosecutor? appeared first on Tech Wire Asia.

Thailand is leading the region in mobile network technology as it has become the first country to launch ASEAN’s first 5G Smart Hospital. 

The collaboration with Huawei Technologies aims to bring patients a more convenient and efficient experience by introducing 5G, cloud, and artificial intelligence (AI) technologies. This includes promoting Siriraj Hospital to become a model for smart hospitals in Thailand and the world.

ASEAN PATIENTS PREFER A DIGITAL HEALTHCARE EXPERIENCE

Aaron Raj | 28 September, 2021

First and largest 5G smart hospital in ASEAN

According to a press statement from Huawei, the 5G smart hospital project is the “first and largest” in Thailand and the Southeast Asian region. The new digital health care facility is located in Bangkok and will showcase various innovative digital applications. 

Both Huawei and the Siriraj Hospital strengthened their partnership around 5G since the pandemic started. A five-year memorandum of understanding of an immediate upgrade of the hospital’s previous infrastructure and the conduct of demonstration exhibitions and events to promote 5G was signed by both parties recently.

The Siriraj World Class 5G Smart Hospital project comprises nine sub-projects comprising smart emergency rooms and emergency medical service, a pathological diagnosis system with 5G and AI, an AI platform for non-communicable diseases, smart inventory management, a permission-based blockchain for personal health records, smart logistics with a 5G self-driving car, multi-access edge computing and a hybrid cloud system.

Siriraj Hospital and Huawei also stated that they established a Joint Innovation Lab to incubate innovative 5G applications. It is expected that 30 5G medical applications will be incubated and promoted nationwide in 2022.

The first step in the utilization of digital technologies

“The Thai government understands the importance of technology, successfully drafting a plan for Digital Thailand. The utilization of digital technologies and 5G in the medical field will help reduce processes for medical personnel, decrease overall risk, and will improve the effectiveness and efficacy of healthcare for patients, Thailand Prime Minister and Minister of Defence General Prayut Chan-o-cha said during his address on the national policy on 5G technology and digital economy.

He added that Siriraj 5G Smart Hospital is a pilot project, which will expand to other hospitals in the future. 

Abel Deng, Chief Executive Officer of Huawei Technologies (Thailand) Co., Ltd., highlighted that this cross-sector collaboration will enhance and upgrade the services of Siriraj Hospital to progress it to become a smart medical centre using digital technologies based on 5G, AI, Big Data infrastructure, and Cloud Edge processing for patient tracking, disease diagnosis by AI on Cloud, data storage and analysis, and allocation of resources.

All of which will help pave the way to fulfil the Faculty of Medicine Siriraj Hospital’s vision to become the “Medical Institute of the Nation, a Creator of Global Citizen Wellbeing”, and a Smart Hospital model for other hospitals in Thailand,” he said.

“This signifies a model for upgrading Thailand’s public health industry in the future and contributes to Siriraj’s transition to becoming a Smart Hospital, in line with Huawei’s mission to Grow in Thailand, Contribute to Thailand,” said Deng.

The post Thailand, Huawei unveil ASEAN’s first 5G “Smart Hospital” appeared first on Tech Wire Asia.

About two months ago, Alibaba Cloud unveiled a new server chip that’s based on advanced 5-nanometer technology. It was dubbed as China’s most advanced chip, a milestone in the country’s pursuit of semiconductor self-sufficiency. The tech giant did not stop there. Adding on to its in-house chip collection, Alibaba apparently has just developed the world’s first, DRAM-based 3D chip that merges logic and memory.

According to Chinese news outlet Pandaily, through a now-deleted post on Chinese social networking site Weibo, Alibaba through its DAMO (Discovery, Adventure, Momentum, and Outlook) Academy has successfully developed a 3D stacked in-memory computing (IMC) chip that can help overcome the von Neumann bottleneck, a limitation in chip speed resulting from the CPU needing to wait for data to be delivered from memory.

The new chip apparently also meets the needs of artificial intelligence (AI) and other scenarios for large bandwidth, high capacity memory, and extreme computing power. In one specific AI test,  the chip improved regular performance by over 10 times.

Clarifying on the chip since the post was deleted shortly after it was posted on Weibo, Alibaba told The Register that the breakthrough chip exists but they declined to provide additional details.

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For context, computers have been designed according to the von Neumann architecture for the past 70 years. Through this architecture, programs and data are held in a memory unit, while the CPU works as a separate unit. Because the memory and processor are separated, data needs to be moved back and forth between the two, making latency unavoidable.

Now,  given the development of advanced processors that can operate much faster than data can be fed to them, the von Neumann model can get quite challenging. At this point, the computing power of processors is increasing at a rate of about 3.1 times every two years. Memory performance however is only rising at a rate of about 1.4 times during the same period.

Reports indicated Alibaba Cloud believes that IMC is the best way to solve these problems. In fact, Alibaba believes this new architecture could be used in virtual/augmented reality, astronomical data calculation, unmanned driving, remote sensing data analysis, and other scenarios in the future.

Alibaba has been part of a growing cohort of technology companies in China venturing further into semiconductors, partly spurred by President Xi Jinping’s goal of achieving self-sufficiency in high-tech sectors. 

To recall, search-engine operator Baidu in August announced it has begun mass production of its artificial intelligence chips, while handset maker Xiaomi is pushing into similar ventures. In the West, tech giants like Apple, Amazon, Facebook, and Tesla are all shunning established chip firms and bringing certain aspects of chip development in-house, based on company announcements and media reports.

The post The newest chip by Alibaba is the world’s first – here’s why appeared first on Tech Wire Asia.

The growing need for digital skills is most acute in developing countries, especially in the ASEAN region. Meanwhile, the cybersecurity skills shortage is especially prevalent in Malaysia and Indonesia, seeing rapid internet adoption rates and a growing need to protect data from cybercriminals.

Cybersecurity is at the top of global leaders’ minds. For years, experts have warned about how digital vulnerabilities are placing businesses and governments alike at risk.

A recent report from Juniper Research found that investment in IT security will grow by almost 6% each year through 2020. The study estimates that this will lead to a need for 1 million more information security analysts than currently employed worldwide.

Despite organizations’ best efforts to find people who can fill roles, there are no signs of the cybersecurity skills shortage abating in the region.

ASEAN AND GOOGLE.ORG TEAM UP TO COMBAT TECH SKILLS GAP

Joe Devanesan | 3 July, 2020

Lack of cybersecurity professionals

The cybersecurity industry is expected to be worth $170 billion by 2021, yet experts warn that businesses struggle to attract talent. Apart from the skills gap, other factors contributing are the growing demand for cyber experts. One way to solve this is re-skilling and upskilling. Many employees in traditional fields are asked to learn new skills. It is either because their existing roles will become obsolete or they need additional qualifications to fill cybersecurity positions.

Each ASEAN country has its own set of challenges. While Singapore and Malaysia have implemented new cybersecurity laws to help close the gap, there are still gaps in training and skill development. 

According to a recent study, there is a lack of skilled cybersecurity professionals, and the talent pool is expected to shrink further over the coming years. The study, commissioned by Oracle and conducted by IDC Asia Pacific, found that just 5% of IT professionals in the region have the technical knowledge and experience to analyze attacks on their networks.

The cybersecurity skills shortage is a growing problem for organizations finding highly-skilled engineers, IT security specialists, and other professionals, especially those trained to work with the latest technologies. With increasing demand from clients for high-end security services, some organizations are having difficulty recruiting competent cyber experts.

Skills shortage – job vacancies yet to be filled

A Digital Talent Survey released by SCMO and SERI highlighted the top two barriers to having adequate digital talent – skills gaps in the local labour market and the inability to attract specialized talents. 

Only 4.8% of respondents felt that the existing labor market fully meets their digital talent needs. Employers reported the most significant skills gaps in Big Data Analytics, Data Science, Cybersecurity, Artificial Intelligence, Cloud Computing.

According to the leading global jobs portal Indeed, hiring activity continued to grow throughout October this year in Singapore. It reached a new high despite the escalating rise in the number of COVID-19 cases. 

By October 30, job postings on Indeed were up 76% from their level on February 1, last year. Postings rose by 4.7% in October alone.  

The reality is, today, more than ever before, organizations are desperate to find skilled IT candidates. But it’s not just about hiring; companies need to keep training them.

According to a report commissioned by Amazon Web Services, the number of workers requiring digital skills in six APJ countries (Singapore, Australia, India, Indonesia, Japan, and South Korea) needs to increase five-fold to meet the demand by 2025.

Investing in skills training can provide new employment opportunities as many workers across the region consider their career goals part of the ‘Great Resignation’ trend spurred by the COVID-19 pandemic. 

Several tech vendors are already working with organizations and higher learning institutes to develop more talent to address the problem. The only question now is, will they be able to do it fast enough to met the demand.

The post Massive cybersecurity skills shortage in ASEAN appeared first on Tech Wire Asia.

E-CommerceDigital retail today is an omnichannel experience that allows consumers to easily maneuver and engage with a business’s platform. This, in turn, provides the information and education to facilitate purchasing decisions. 

In the second part of an interview with Andrew Martin, head of AI & data company Databricks, Tech Wire Asia sought to understand more about the challenges digital retailers face, and how SMEs can address them with data and AI. 

What are the challenges that digital retailers commonly face? 

While expectations surrounding the customer experience have certainly risen to new heights, few retailers have invested in the right technology for meeting these new standards. 

From our research, we’ve identified four customer challenges SEA retailers face. They include fraud, delivery theft, returns, and customer service. 

These aren’t only common, but significantly impact their bottom lines. 

So how can data and AI address these four challenges?

Fraud

Fraud has become too commonplace — SEA stands to lose US$260 million annually to online fraud, with Indonesia, Thailand and Vietnam expected to be the most heavily affected.

Losses associated with fraud soared to US$56 billion in 2020 globally and accompanied a huge dip in customer confidence in the brand. 

Data and AI can help retailers get ahead of fraud and avoid financial and reputational damages, especially when it comes to proactive approaches. 

A modern data architecture that brings data together from geospatial data to sales trends, across the business can effectively enable anomaly detection at a massive scale to protect losses caused by fraud in real-time with machine learning.

Delivery Theft

Logistics costs are high in most SEA nations due to geographical challenges, the imbalanced concentration of economic activities, and poor connectivity between various parts of the countries. 

The lack of adequate shipping infrastructures will make it difficult to deliver parcels within the promised delivery timeframe. This increases the risk of package theft, which is a significant operational burden on retailers, with global estimates of over a million packages being stolen or lost daily. 

Data and analytics can help logistics providers identify common sites of traffic accidents or package thefts and design their services around those. 

The global shipping industry is also using AI to enhance security measures, both within and outside of business grounds, with shipping carriers using drones to patrol the grounds around their warehouses to collect real-time information and data. Data and AI allow retailers to easily identify such hotspots and frame apt responses.

Returns

Without the tactile experience of brick-and-mortar shops, consumers are returning their online purchases at an alarming rate. According to industry data, at least 30 percent of all products ordered online are returned compared to only roughly 9 percent bought in bricks-and-mortar shops. 

The ability to uncover trends in that data with the power of machine learning allows retailers to better understand customer behaviors, spot high-return items and take action with data to minimize returns. 

To any e-commerce retailer, returned products mean additional shipping costs, which can constitute a significant portion of any retailer’s operating margin. Retailers can also incorporate predictive analytics using data and AI in their returns and reverse-logistics operations, to improve service levels with fewer queries and reported issues.

Customer Service

Customer satisfaction, customer retention, and cost to serve are three factors that can define the long-term profitability for retailers. Although customer issues can be wide-ranging, many issues will be common to reoccur amongst customers. 

For example, Natural Language Processing (NLP) tools can be used to quickly analyze service call notes and easily identify the straightforward and most common issues. These can be tackled by blending digital and call center channels and driving self-service usage for common queries. 

Finally, businesses can arm their customer care teams with visual data snapshots of helpful customer insights – an at-a-glance view of the context, key data points in their history, and next-best-step suggestions while the customer is on the call. This is especially useful while dealing with an at-risk customer identified by machine learning (ML) models who have been routed to the retention specialist.

Are there other challenges that SEA retail SMEs face today? 

Across SEA, countries are still battling uneven vaccination rates and are still dealing with outbreaks and lockdowns. While regional online retail has skyrocketed, SEA economies continue to rank poorly in being able to prepare and adapt to the pandemic’s changing conditions. 

When looking at the retail industry holistically, one needs to be mindful that logistics, supply chain and last-mile delivery are also as critical as the products and services sold and the user experiences provided. With SEA’s retail and consumer goods markets in flux, accurate forecasting that considers variations in day-to-day product demand and distribution will be essential.

Accounting for these shifting market conditions is often well beyond the capabilities of legacy, data warehousing-based tools. The retail industry and related organizations working with ever-growing, day-to-day digital data will need a centralized hub — a logistical control tower of sorts — to orchestrate the technology, tools, and processes used to capture data across all stages of the supply chain. 

The demand for more granular, timely forecasting, can be met with solutions that employ data insights derived from machine learning. These can power the retail industry to generate forecasts that move away from traditional linear models and historical-based algorithms, towards flexible inventory planning that can be precisely adjusted on an individual day and store level.

The post AI can solve your online retail woes appeared first on Tech Wire Asia.

According to a report from the Malaysian Investment Development Authority (MIDA), Malaysia remains a key regional destination for high-value manufacturing and global services. This shows the enormous growth potential the nation has for those areas, and the continued need for industries to begin their digital transformation journey if they have not.

IBM, SAMSUNG AND M1 TEAM UP ON 5G SMART MANUFACTURING TRIAL IN SINGAPORE

Joe Devanesan | 11 May, 2020

The reality is though, for smart manufacturing to be successful, businesses need to first look to implementing smart systems.

“The fact that the manufacturing industry has tended to maintain legacy systems and processes. Since last year, businesses have faced new challenges in their work including supply chain disruptions, and new social distancing protocols, just to name a few, which have reduced their efficiency and in the long run, impacted production,” said Steven Loh, Senior Sales Director, Relationship Segment at Lenovo Malaysia.

Steven explains that businesses must evaluate their manufacturing chain to keep up with the challenges, but it is not a matter of browsing a catalog and picking out the latest technology to implement. There are several considerations that businesses must look at when deciding what type of technology that they should invest in:

• Businesses must understand their business challenges – A business needs to understand the end-solution they intend to achieve; whether it is to optimize productivity, reduce operational costs or improve the customer experience. It is only when they have identified their primary objectives and challenges can they identify the technology that they need to invest in.
• Investing in Smart Systems – Technology is everchanging and there will be newer innovations that are introduced. However, for larger businesses, overhauling systems in favor of new ones in a short cycle may not be sustainable. As such, they must consider investing in products with increased usability through predictive AI/Machine Learning.
• Engaging an expert to evaluate business solution needs – Employing an expert can help you in evaluating your business needs and the type of solutions that would work best. This helps reduce the concerns for the business owners and ultimately, saves them from investing in the wrong technology which could be detrimental in the long run.

Interestingly, Steven pointed out that beyond the cost, businesses might be reluctant to adopt newer technology due to several factors. The conversion of systems from the current ones to a new system is often a complex process. For many businesses, these systems would have been in place for many years or are a legacy system. Businesses would be even more hesitant especially if the system has worked out for the businesses to date. However, as the demands of the global marketplace change, businesses that do not adapt may fall behind.

Steven Loh, Senior Sales Director, Head of Relationship Segment, Lenovo Malaysia

Separately, as new systems are implemented, Steven highlighted that retraining of staff must be conducted to ensure that they can operate the new systems. The amount of money and time invested in retraining these staff may lead to reduced production output during the first few months that the new systems are implemented. If businesses look at the long-term benefit, they may be met with increased efficiency, agility, and productivity from the new systems.

“Recently, Lenovo worked with a local Malaysian corporation to adapt, innovate, and scale with agility to meet the rising demand for their product amidst the pandemic. By working with Lenovo, the customer was able to deploy a high-availability infrastructure allowing factory production to run uninterrupted, non-stop around the clock, without having their employees work overtime and allowing them to work-from-home in order,” added Steven.

At Tech World 2021, Lenovo also announced the expansion of their “Everything-as-a-Service” business model with Lenovo TruScale to provide a truly global solution that makes everything, from the pocket to the cloud, accessible via a single contract. Lenovo TruScale now offers businesses of all sizes the flexibility they need to stay competitive with a scalable, cloud-like consumption model and predictable payment options for hardware and service inclusions. This transition to a fully integrated as-a-service strategy brings to life the company’s vision of bringing its smart devices, smart infrastructure, and smart solutions businesses together in a new way for customers – addressing common business challenges and providing IT leaders with the performance and flexibility to pay-as-you-grow.

“With Lenovo TruScale, IT leaders’ infrastructure solutions are fully managed, giving customers the advantages of an on-premises cloud environment along with the peace of mind around data management in a hybrid environment,” explained Steven.

5G and smart manufacturing

The future of IT is strongly rooted in the adoption of 5G. In fact, according to PSB Research, 91% of IT managers believe 5G will drive new products and services that have yet to be invented. Moreover, a report from IHS Economics predicts that the 5G value chain is expected to contribute 3.5 Trillion USD of new economic output and 22 million jobs by 2035. Today’s technological challenges only further emphasize the value of this next-gen technology, positioning its integration as not only a timely solution but a representation of what is to come.

“Companies around the world continue to gear up for 5G. We are also investing in 5G for its faster connectivity, greater capacity, ultra-low latency, and improved reliability and flexibility, suitable for applications such as IoT infrastructure, rural/edge enablement, and AR/VR.  Though exceedingly valuable, the adoption of 5G is complex, costly, and time-intensive. Lenovo is working on making the process more affordable and efficient by providing a variety of devices and different price points,” highlighted Steven.

As 5G will enable smart systems to improve smart manufacturing, most use cases are being developed and tested among Malaysian companies as well. This included the use of autonomous robots such as cobots in packaging, automated machinery in factories as well as smart sensors to provide real time statistics.

The post Enabling smart systems to ensure smart manufacturing in Malaysia appeared first on Tech Wire Asia.

Despite that, the technology can learn and harness data faster, providing better results in the use cases it’s being implemented in. Be it taking photographs, driving a car, ordering food, and doing transactions online, there are elements of AI working in the background all the time.

CHINA IS WAY AHEAD IN THE AI GAME, LEAVING THE US BEHIND

Dashveenjit Kaur | 15 October, 2021

For enterprises, AI has enabled them to not only improve their productivity but also have better analytical insights and also make data-driven decisions. Together with cloud computing, businesses are deploying AI to achieve IR4.0.

However, with greater technologies, comes greater concerns as well. With AI technology still growing and learning, there are concerns about the ethics that come with AI. As AI feeds primarily on the data it has access to, there have been issues on AI potentially eroding trust, exacerbating inequity, and causing harm to both people and the environment.

While it is not a case of machines trying to take over the world, some AI algorithms have been known to be biased and being unethical. As such, some consumers are having less trust in the tech industry despite the increasing adoption of AI in most business use cases.

The World Economic Forum and the Markkula Center for Applied Ethics at Santa Clara University published a case study highlighting processes, tools, and organizational constructs that facilitate the responsible design, development, and implementation of technology. The new white paper recognizes IBM’s leadership as a trusted, responsible technology company, particularly as a leading developer of ethical AI.

To understand more about ethical AI and its impact on both consumers and businesses, Tech Wire Asia spoke to Seth Dobrin, Chief AI Officer, IBM.

In the first part of this interview, Seth explains what ethical AI is all about and how IBM is implementing it in its solutions for use cases around the world. Seth also shares how blockchain and ethical AI are linked.

What exactly is ethical AI?

AI ethics are an essential part of building an AI that is trustworthy, but they are just one piece of the puzzle needed to help people and organizations adopt AI responsibly. We focus on a human-centered approach to trustworthy AI, an approach that puts ethical principles at the core of our governed data and AI technology and fosters an open and diverse ecosystem.

When it comes to our ethical principles, our guiding values make clear that the purpose of AI is to augment human intelligence; that the data and insights generated from data belong to their creator; and that powerful new technologies like AI must be transparent, explainable, and mitigate against harmful and inappropriate bias.

It’s easy to say that ethics matter, but actually embedding those ethical principles into the technology itself is more complex. That’s why the second pillar to our approach is to continually bring innovative governed data and AI technology and approaches to market that is built on five focus areas: explainability, fairness, robustness, transparency, and privacy.

Finally, we believe that for AI to be successful, it must be built in an open and diverse ecosystem. Delivering on that means fostering a culture where diversity, inclusion, and shared responsibility are imperative.

AI is already transforming how businesses operate and engage the world, delivering the power of prediction to augment human decision-making. However, humans must be able to trust predictive recommendations and outcomes for AI to realize its full potential. That’s why IBM believes it’s essential to consider AI ethics as one key component of the holistic approach that’s required to build trustworthy AI.

How do vendors define it in their solutions, especially when dealing with customers?

Organizations today recognize that it takes a holistic approach to manage and govern their AI solutions across the full AI lifecycle. There are good evidence companies and their customers are on the same page when it comes to the need to achieve fairness and reduce bias. A recent survey showed 91% of businesses using AI today say their ability to explain how it arrived at a decision is critical and 86% agree that consumers are more likely to choose the services of a company that is transparent and ethical about how the AI they use is created and managed.

(Photo by MIGUEL MEDINA / AFP)

That said, while the data shows global businesses are now much more aware of the importance of having trustworthy AI, more than half of companies cite significant barriers in getting there including lack of skills, inflexible governance tools, biased data, and more. It’s clear while there are tools and frameworks in the market to help build trustworthy AI, there is still work to be done to help businesses develop a comprehensive approach to AI governance.

That’s our current focus at IBM. We’re working to continually bring innovative governed data and AI technology and approaches to market that are built on five focus areas: explainability, fairness, robustness, transparency, and privacy. Our solutions for trustworthy AI help businesses with things like auditing and mitigating risk, implementing governance frameworks, operationalizing AI, education and guidance, and organizational change. Organizations ranging from a large American retailer to financial institutions like Regions Bank and sports organizations like ESPN Fantasy Football are putting the principles of trustworthy AI to work.

Is there a link between blockchain and ethical AI? 

Blockchain is a shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a business network. Almost anything of value can be tracked and traded on a blockchain network. Blockchain is ideal for delivering that information because it provides immediate, shared and completely transparent information stored on an immutable ledger that can be accessed only by permissioned network members.

AI, until recently, was not built with issues like security or transparency as primary goals. But, at IBM, we see these components as central to building trust in the technology. That’s why our approach to ethical and trustworthy AI is built on our five focus areas: explainability, fairness, robustness, transparency, and privacy.

That said, in the second part of our article, Seth explains how ethical AI deals with AI biasness in some use cases and how businesses can deal with them.

The post Can AI ever be “ethical”? appeared first on Tech Wire Asia.

But as technology evolves, so will associated costs, making them far more accessible to the masses. This is what’s happening with retail — as more consumers shop online, the time is ripe for micro, small and medium enterprises alike to utilize AI in retail, especially in e-commerce.

Tech Wire Asia had the opportunity to speak to Kaushik Sriram, Partner at Kearney, on how the use of AI in retail can help MSMEs thrive and remain competitive during a pandemic.

Could you briefly explain how AI can be applied to retail?

There are two common and relevant uses for AI in retail — the first pertains to the use of predictive analytics for modeling, and the second is in conversational AI. 

Predictive analytics serve to analyze historical data, discover patterns and observe trends. These functions are incredibly useful to help users predict future trends to help sellers determine what can sell well. 

When it comes to conversational AI (such as chatbots, messengers, etc.), they serve to augment the customer experience journey at various stages of the marketing funnel.

It facilitates the customer service offering that goes beyond enquiries, right into transactions, which makes customers more likely to enjoy your service and return to your store. 

What can predictive analytics do for retailers?

Typically, predictive analytics features in models for marketing and sales, and businesses can easily start efforts with simple rule-based algorithms. 

For the retail sector, especially in verticals such as fashion, these insights are particularly impactful, because the cash investment in these physical goods is high, and demand may greatly fluctuate and cause inventory issues.

For example, if the retailer stocks too many pieces of certain styles that may go out of fashion, they would be left with overstock that would be difficult to sell-off. 

And during covid times like these, MSME retailers cannot afford to make the wrong choices when it comes to inventory stocking.

Getting rid of slow-moving goods may result in lowered revenues, such as when prices are heavily slashed to quickly get rid of stock to make space for newer ones.

Predictive analytics can help ameliorate these issues with demand forecasting in order to optimize inventory planning.

Demand forecasting sounds crucial but expensive. Is this true?

Well, demand forecasting informs supply planning. Contrary to popular belief, you don’t exactly need to have detailed customer data to use this. 

You already have such information in your store, such as product (SKU) sell-throughs or sales volumes tracked during certain periods of time (e.g. paydays, 11.11, holidays, etc.). 

These data are immensely powerful, and it is important to mine them to inform promotional or pricing strategies down the line.

Interesting. So how about conversational AI?

These days, customers’ needs are quickly changing, and it is increasingly showing that customers want more personalized, interactive experiences where their enquiries or concerns are seen as quickly as possible. 

70% OF SINGAPOREAN CONSUMERS WANT A PERSONALIZED DIGITAL CUSTOMER EXPERIENCE

Jamilah Lim | 19 August, 2021

Those are applicable to both brick and mortar stores, as well as online, although arguably it is easier to maneuver for purely online retailers due to the ease of collecting customer information. 

Are there other applications for AI in retail?

Specifically for brick and mortar stores, AI in retail can be used to identify traffic footfalls. This can be achieved using image processing solutions to understand customer visit patterns and optimize operations in terms of manpower and inventory stocking. 

When it comes to MSME retailers, customer engagement, and marketing and sales analytics are important — they make up about 40 – 45 percent of business value for the retail sector.

Digitalization of customer engagement is key, and this can be achieved with just a simple AI chatbot. It will help keep the customer engaged throughout their purchase journey. 

From placing orders, to checking delivery statuses — these go towards reducing friction. Conversational AI bots can be used to manage and stack orders, even. 

What are the challenges that MSMEs face with analytics?

Online sellers primarily utilize two sales channels — an independent website, or through an e-Commerce platform. 

SaaS analytics can be used on independent sites, whereas e-Commerce platforms can offer sellers analytics services to help tailor products listed to maximize their relevance to customers.

However for offline-only or hybrid retailers, when it comes to sales and marketing analytics, they often don’t have sophisticated POS (point-of-sale) terminals, which impedes the ability to collect and use data, such as when you need to improve assortment choices to target specific customers. 

Information such as billing info, order, value, and even loose tagging of the type of customers will be useful — both for brick and mortar, or solo online stores.

So what can MSME retailers do to optimize the use of AI in retail?

The good news is, that there are a lot of cloud-based SaaS options that come with analytics out of the box. As they’re cloud-based, you don’t have to worry about losing data stored on-premise (e.g. when errors or accidents occur), or when scaling up or integrating more sophisticated applications.

Furthermore, these data often come with easy-to-understand visualizations to help with quicker decision making, and the analytics can be accessed anywhere, across different devices.

It would be wise for retailers to also look at upgrading their POS terminals to be able to capture better data — there are plenty of third-party vendors that offer such services.

When it comes to AI SaaS, depending on what your business needs, MSME retailers can start small with basic packages, and then add on more features when they scale up.

I must stress that it is important to do the necessary research and comparison between vendors and service providers before jumping into choosing the most popular or well-received one, however.

Different retailers have different needs, and it boils down to identifying and choosing what your business needs at which point of time it is in. And this is not just when you are finding a solution or product at the beginning of your AI journey, or for the short term — you should also assess if their offerings are able to support scaling up when your business grows.

The costs to implement these may be a little higher in the beginning, but the payoff will be worth any retailer’s time and investment in the future.

The post AI in retail no longer an option, but a necessity for MSMEs appeared first on Tech Wire Asia.