Predictive decisions are always made based on the data and variables that are available. Economies around the world continue to analyze data available to them to predict market outcomes and guide the likes of interest rates, on which those economies depend for their continuation. But the thing about predictions is that they will always remain predictions, rather than certainties. Probabilities can, and do, still go either way.

Technology is now slowly changing this, though. While AI has made a difference to businesses by automating processes, it is also capable of studying data to provide insights. These predictive analytics are the process of using data to forecast future outcomes. By using data analysis, machine learning, AI and statistical models, businesses can find patterns that might accurately predict future behavior.

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The use of data to predict outcomes is at the core of the financial world. Analysts and economists have been using various models and calculations to predict marketbehaviors for decades. While they do hit the mark, most times, the process can be long and tedious.

At the same time, there is also default probability analytics. Default probability is a crucial concept in credit risk analytics. It quantifies the likelihood that a borrower or counterparty will default on their financial obligations within a specified time frame. Expressed as a percentage, it represents the probability of a credit event occurring.

Here’s how it works:

1. Data Collection: Relevant data about borrowers and their credit performance is collected. This includes financial statements, credit bureau reports, loan repayment history, and other relevant factors.
2. Data Pre-processing: The collected data is cleaned and prepared for analysis by removing duplicates, correcting errors, and addressing missing values.
3. Variable Selection: A set of predictive variables (features) is chosen based on their relevance to default prediction. These variables include financial ratios, credit scores, industry indicators, macroeconomic factors, and other relevant information.
4. Model Development: Statistical models are built to estimate the probability of default. These models use historical data and consider financial health, credit history, and economic conditions.

What the risk map looks like. (Source – Kamakura).

SAS and Kamakura

This is where companies like SAS and Kamakura come in. Having acquired the risk management software, SAS now provides an unparalleled suite of integrated risk solutions, particularly concerning asset liability management (ALM) and other essential solutions for the financial services industry. SAS has scaled resources to support Kamakura products, enabling SAS resources and selected specialized partners.

For default probability analytics, Kamakura provides default probability measures for public firms, non-public firms, US banks, and sovereign counterparts which can be used to assess the creditworthiness of an entire credit portfolio or on a single name basis. Inputs to the Kamakura models include company-specific attributes, industry-related measures and relevant macro-economic factors. Independent tests have confirmed that Kamakura default probabilities have the highest-performing predictive power available in the market.

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According to Donald R. Van Deventer, the founder of Kamakura, the software product is Kamakura Risk Manager. It’s an enterprise-wide Risk Management System (KRIS) that integrates market risk, asset liability management, credit risks, transfer, pricing, capital allocation, and many other variables. Van Deventer stated that the alliance with SAS, especially with the SAS Viya platform, makes the hosting of Kamakura Risk Manager (KRM) seamless, and facilitates the integration of KRM with other products.

Specifically, KRIS default probabilities are updated daily for 40,500 firms in 76 countries, for more than 4,900 US banks insured by the FDIC, and for 183 sovereigns. The legacy rating agencies update ratings on only 3-6 corporate families per day. Worldwide, only around 2,650 corporate families have ratings.

The solution works by considering the average percentile wreck of the defaulting observations. Put simply, it’s the universe of default probabilities of the observations that did not default.

“We value every traded corporate bond in the US corporate market every day. So the best way to measure accuracy is not to make up some absolute standard. For the Challenger model, we’ve chosen a rating base valuation model, where every day, we ask the question, what credit spread best fits all the bonds with a triple eight rate or what credit spread fits the bonds with a double A plus, and so on,” explained Van Deventer.

Kamakura’s Implied Rating model provides a most likely legacy rating agency rating for a public firm based on company-specific attributes, Kamakura default probabilities, industry classification and relevant macro-economic factors along with the historical behavior of the legacy rating agencies.

Bonds. (Source – Kamakura).

Default probability analytics: the opportunity in Southeast Asia

Van Deventer was recently in Southeast Asia, speaking to banks and potential customers about the benefits of using this model. In fact, he believes the region has a lot to gain, given the amount of data available.

“People in the region are very smart. People realize how much volatility in the region is coming out of China, both political and economic. And they need to have a tool that can immediately respond if China does something strange. I don’t have the probability for political acts by China or any country although we do have a sovereign risk estimate for China. And the fall probably we see from China is higher than any other country in the region with around 3% annual rate,” he explained.

What this means is that financial services in the region, especially the banks, would be able to use the model to predict their market in the future. By using KRIS, banks will have a very multinational database to bear.

“Blind, stupid, doodah, clueless luck!” – Sure, but imagine if you had the power, and the data, to accurately predict every toin coss of investment or financial provision. Welcome to default probability analytics.

The post How default probability analytics can make a difference  appeared first on Tech Wire Asia.

CIOs around the world face different challenges, especially with the increasing adoption of technology by organizations. In 2023, there were two areas that dominated most discussions CIOs had with their boards. The first was on how much more budget businesses should allocate towards cybersecurity, and the second was on how much generative AI should the organization should be looking to implement – and what to expect in return for their AI investment.

Generative AI can be considered the biggest challenge for CIOs in 2023, simply because of the ways in which employees used the tool for work without proper guidelines and jurisdiction. Increased concerns about company data being exposed needed to be highlighted, with some organizations eventually banning or limiting the use of ChatGPT or similar generative AI tools at work.

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According to Amir Sohrabi, regional vice president and head of digital transformation, emerging EMEA and Asia at SAS, generative AI has been an extraordinary leap forward for employee productivity, but it is also driving the use of tools that do not have the blessing of IT departments. IDC expects an increase in AI spending next year across the Asia Pacific, which means CIOs could be even more challenged in 2024.

“If this sounds familiar it’s because where CIOs previously struggled with “shadow IT,” today they need to account for “shadow AI” – the use of AI by a company’s employees that is unsanctioned by its IT department. Generative AI also requires significant guardrails so expecting CIOs to give in to unfettered adoption of these tools is out of the question,” said Sohrabi.

“As we see the reality of what AI can bring to the table, it’s also important that you have governance, scale and control.”

Shadow AI is a term that describes AI systems that are not officially known by or under the control of the IT department of an organization. It can happen when employees use or create AI applications without the approval or supervision of the IT department. This can pose various risks to the organization, such as data leakage, security breaches, model theft, or ethical issues.

Shadow AI can also hinder the coordination and integration of AI solutions across the organization, so IT leaders need to have clear policies and strategies to manage shadow AI and ensure its responsible and effective use.

Sohrabi believes that guardrails will still not stop employees from using tools like ChatGPT and Bard, which have enormous benefits for productivity. As such, CIOs in the region will need to grapple with this and study mitigation measures that are in line with their organization’s risk tolerance. Ultimately, well-intentioned employees will turn to these tools to be more efficient, and CIOs can ensure this doesn’t jeopardize the organization by proactively taking charge of the situation.

Shadow AI could be a big problem for CIOs in 2024. (Image generated by AI).

Levelling up data literacy and storytelling

“Immense technological change and the looming threat of a recession are driving businesses to look for new ways to operate more efficiently, while also increasing the ‘stickiness’ of their customers. The consensus among enterprises is that they need to make the most of technology to lower operating costs while maximizing efficiency and pursuing opportunities to grow the customer base,” explained Sohrabi.

As such, for CIOs to drive enhanced efficiencies and increased productivity, Sohrabi pointed out that they must have a data-first mindset. That’s because getting the most out of tools like generative AI hinges on having data in order – which entails good data management practices, including data access, hygiene and governance.

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“Organizations that lack these best practices will find that generative AI isn’t as productive for them because it does not have accurate and well-managed data to act on. Good data management also improves customer experience, and facilitates innovation, while positioning the business to capitalize on cost-saving opportunities. Expect CIOs to take the reins to empower organization-wide data literacy,” he said.

At the same time, Sohrabi highlighted that as CIOs are integral to corporate strategy today, they need to get comfortable opening up to their C-suite peers and wider organizations about their challenges and wins. The reality of the situation is that a strong IT organization is now imperative because new technologies are being deployed on a near-daily basis.

“2024 will see CIOs lean into what they do best – using data and analytics to tell the story of the impact they’re having on corporate strategy. This will see CIOs detail how challenges were addressed through people, processes, and/or technology – which will also go a long way to building trusted relationships with colleagues throughout the enterprise,” Sohrabi said.

CIOs must have a data-first mindset. (Image generated by AI).

From Shadow AI to technical debt

While Shadow AI might be a concern, there are also some positives from businesses using more technology. However, as businesses migrate more of their digital infrastructure to the cloud, key questions around security, privacy, unauthorized usage, as well as access management and resource provisioning will continue to dominate.

“Expect this to have a major impact and drive the growth of financial operations (FinOps) as a means for businesses to reap the benefits of managed services from cloud service partners. At its core, FinOps is the practice of managing cloud spend by leveraging the inherent variability of cloud use. This is akin to turning off the lights at home when no one is using them. Failure to take advantage of this feature is akin to treating a public cloud as a very expensive data center.

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Typically, technologies, services and infrastructure are always on in the average corporate data center. But just because IT departments are used to this model, doesn’t mean it should persist. With 2024 likely to be a year of opportunities and challenges in equal measure, expect businesses to take advantage of variable usage to enable significant savings,” said Sohrabi.

Given the opportunities and challenges of using AI and FinOps, there also needs to a be focus on technical debt. For example, as some development teams take shortcuts in writing code to achieve their goals faster, this can result in technical debt. When it accumulates, it can slow future development and cost more to rectify.

“Combating technical debt starts with robust continuous lifecycle management programs that categorize technology into understandable groups IT can act upon. However, reducing technical debt through lifecycle management is useless if more debt is added. That is why a committed enterprise architecture program to help map technologies to business processes sets the foundation for technology standardization. Expect CIOs to enforce adherence to enterprise architecture target states and technology standards,” added Sohrabi.

Sohrabi believes that 2024 will definitely be a year of significant challenges. But he also believes that there will be opportunities, especially for businesses that are quick to the punch and able to withstand disruption.

“CIOs will be critical to this, as they can make a difference by leading the integration of new, emerging technologies and trends with business strategy. This will be crucial for driving businesses to refine operating models that enhance business operations and provide products and services which engender customer loyalty, for business advantage in an ever more competitive landscape,” he concluded.

What does the future hold? Let’s see what could be on the horizon in the next iteration of artificial intelligence. Here are 12 predictions from SAS 💭 . https://t.co/UPm8X54FFX #AIpredictions #SASpredictionsAI pic.twitter.com/GGzu2JBZfv

— SAS Software (@SASsoftware) November 30, 2023

The post Shadow AI and tech debt: IT priorities for the next phase of digital transformation appeared first on Tech Wire Asia.

Companies are constantly navigating the labyrinth of multiple vendors for different systems, each introducing unique dynamics, interfaces, and requirements to their operational matrix. Beyond the surface-level management of these diverse services, there’s an underlying need to synthesise them into a singular, streamlined entity that simplifies operations and amplifies output. This is a particular challenge for those in the Technology Solutions Provider (TSP) sector, who must coordinate multiple systems seamlessly to ensure consistent, top-tier results.

Source ConnectWise

Redefining business efficiency through unified solutions

Managing multiple vendors presents a significant obstacle for businesses, often termed ‘vendor fatigue.’ Vendor fatigue encompasses the difficulties of navigating unique systems and requirements from each vendor. Different interfaces add to the learning curve for employees, leading to potential errors and inefficiencies. Financial teams grapple with diverse and frequent billing, making invoice processing time-consuming and error-prone.

While diversifying vendor relationships might seem like a strategic move to leverage the best from each, the administrative, financial, and technical overhead can quickly escalate and negatively impact the overall operational efficiency of the organisation.

Furthermore, the technical demands are relentless. Constant updates and patches from multiple vendors require intricate coordination, often sidelining IT teams from proactive improvements. The costs and complexities can sometimes outweigh the benefits of such vendor diversity.

Harnessing true efficiency

The seamless flow of data across platforms is paramount for maintaining a competitive edge. However, disconnected systems hinder the smooth flow of data, resulting in inefficiencies that restrict business growth. Risks include misinterpreted data leading to flawed strategies, data loss causing missed opportunities, and data discrepancies affecting analytics.

Manually re-entering data wastes time, is error-prone, and can negatively impact areas like inventory and financial planning. Ensuring consistency across platforms becomes a time-consuming necessity, diverting resources. Fragmented systems can also obscure vital insights, potentially causing businesses to overlook significant opportunities or challenges.

To truly harness efficiency, businesses need integrated, coherent systems capable of delivering accurate and consistent data. Without this, they risk getting bogged down by preventable challenges, missing out on the advantages of true technological synergy.

Positioned for growth

Growth in a company is epitomised by its capacity to adapt, innovate, and strategically progress. It encompasses sustainable scaling and aligning changes with core values. Navigating this growth trajectory requires benchmarks, akin to a compass in a vast, shifting ocean. Without them, businesses risk being reactionary rather than proactive.

Such benchmarks gauge a company’s market position, inform about customer trends, and spotlight best practices and areas for enhancement. Embracing a benchmarking culture equips businesses to define clear, ambitious goals, ensuring steps towards enduring success.

The cloud advantage in the digital age

The cloud signifies a transformative approach to data management, offering superior scalability and flexibility compared to traditional on-premises systems. Whereas old server setups require constant manual upkeep, often consuming valuable resources and introducing downtime, cloud solutions automate many tasks.

According to PwC, companies powered by the cloud are four times more likely to report facing no barriers when achieving value in areas such as improved decision-making, increased productivity, enhanced agility, cost savings, and product and service innovation.

Service providers handle maintenance and updates, granting businesses constant access to the latest enhancements. The cloud’s cost-effective model further reduces upfront infrastructure investments. Moreover, cloud-hosted data and applications ensure anytime, anywhere access, fostering collaboration and guaranteeing business operations, even during unexpected events.

Source ConnectWise

Adopting cloud solutions isn’t just a tech upgrade, it’s a strategic move that liberates businesses from operational shackles, letting them focus on what truly matters: growth, innovation, and delivering unparalleled value to their customers.

A commitment to success in the digital age

This quest for integration, efficiency, and alignment is where ConnectWise truly shines, promising solutions and transformations. This platform is a comprehensive ecosystem, and with everything from sales automation to reporting under one umbrella, ConnectWise eradicates the need to juggle multiple vendors.

The integrated approach of tools like ConnectWise CPQ, PSA, and WisePay through their business management offering, ensures that everything remains within one cohesive system from when a quote is generated to when cash is realised. With ConnectWise, businesses can experience a smooth transition from sales to execution, eliminating redundant data entry and focusing on core business tasks.

ConnectWise’s BrightGauge tool consolidates data across operations into clear dashboards and reports, providing reliable data that informs strategic decisions in real time. Proactive notifications also ensure businesses are anticipatory in their approach.

Furthermore, ConnectWise’s industry-leading benchmarking offers insights that guide decision-making. Tools like SLIQ provide comparative metrics, allowing businesses to gauge themselves against peers, driving motivation and direction.

Customer relations remain paramount, and with ConnectWise’s tools, businesses are always attuned to their sentiments. Streamlined financial processes facilitated by ConnectWise also promise growth, making collections predictable and ensuring financial stability.

But what truly sets ConnectWise apart is its unwavering commitment to the success of its partners. Its Premium Business Management package isn’t just about providing tools, but optimising their use. With monthly virtual consultations, businesses are constantly guided, ensuring they harness the full potential of their resources.

In a world where change is the only constant, having a partner like ConnectWise can be transformative. Its holistic, integrated approach ensures businesses aren’t just functioning, but thriving. With smooth implementation, a focus on ROI, and an unparalleled suite of tools, ConnectWise promises unparalleled business potential. Dive into the ConnectWise ecosystem today, and lead the change you wish to see.

To find out how ConnectWise can support your business, visit the website, chat to the Australian team on 1800 573 165 or get in touch via via the website.

The post How can MSPs truly redefine business efficiency in the digital age? appeared first on Tech Wire Asia.

The demand for high bandwidth memory (HBM) is taking off, thanks to OpenAI’s ChatGPT, which has led to a surge in generative AI servers. US memory chip giant Micron Technology, a tad behind its large competitors regarding HBM, has just unveiled its newest version, an 8-high 24GB HBM3 Gen2. Micron is dubbing it as the most advanced in the market currently, and its HBM3 will be able to speed up the development of generative AI.

First and foremost, although the field of AI has been around since the 1950s, it was not until very recently that computing power and the methods used in AI reached a tipping point for significant disruption and rapid advancement. Today’s applications mean that computing tasks have become more complex and strenuous, requiring faster and more efficient access to data. 

Like all memory, HBM advances performance improvement and power consumption with every iteration. Take generative AI, for instance; that alone holds a tremendous need for much higher HBM. That is why, according to market experts, HBM is a segment of the memory industry that is set to grow by leaps and bounds thanks to the advent of generative AI.

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In a recent research report, Trendforce noted that Micron has a tiny share of this high-growth market, with around 10%, versus an estimated 50% share for SK Hynix and 40% for Samsung. Therefore, to catch up on its major competitors in the segment, Micron Technology decided to tease its newest version of HBM for AI accelerators and high-performance computing (HPC). 

In a virtual press briefing, Micron Technology said it has begun sampling the industry’s first 8-high 24GB HBM3 Gen2 memory with bandwidth greater than 1.2TB/s and pin speed over 9.2Gb/s, starting July 27, 2023. Praveen Vaidyanathan, vice president and general manager of Micron’s Compute Products Group, highlighted that the latest iteration is up to a 50% improvement over shipping HBM3 solutions. 

“With a 2.5 times performance per watt improvement over previous generations, Micron’s HBM3 Gen2 offering sets new records for the critical AI data center metrics of performance, capacity, and power efficiency,” he added.

Unleashing the performance of AI. Source: Micron Technology

Most importantly, these Micron improvements reduce training times of large language models like GPT-4 and beyond, deliver efficient infrastructure use for AI inference, and provide superior total cost of ownership (TCO), Micron said in its statement.

HBM3 Gen2 by Micron Technology in the age of ChatGPT

Because of its costs and complexity, HBM was only a tiny niche market until this year. Research publication Trendforce forecasts HBM demand will soar by 60% this year, with at least another 30% growth forecast for 2024. That being said, Micron decided to develop HBM3 Gen2 with performance-to-power ratio and pin speed improvements critical for managing the extreme power demands of today’s AI data centers. 

“The improved power efficiency is possible because of Micron advancements such as doubling of the through-silicon vias (TSVs) over competitive HBM3 offerings, thermal impedance reduction through a five-time increase in metal density, and an energy-efficient data path design,” the company’s statement reads.

Micron HBM3 Gen2 bringing down the cost of generative AI. Source: Micron Technology

The Micron HBM3 Gen2 solution also addresses increasing demands in generative AI for multimodal, multi-trillion-parameter AI models. “With 24GB of capacity per cube and more than 9.2Gb/s of pin speed, the training time for large language models is reduced by more than 30%, resulting in lower TCO,” Vaidyanthan said. 

Additionally, he added that Micron’s offering unlocks a significant increase in daily queries, enabling trained models to be used more efficiently. “Micron HBM3 Gen2 memory’s best-in-class performance per watt drives tangible cost savings for modern AI data centers. For an installation of 10 million GPUs, every five watts of power savings per HBM cube is estimated to save operational expenses of up to $550 million over five years,” he shared.

Vaidyanathan also mentioned that the foundation of Micron’s HBM solution is its industry-leading 1β (1-beta) DRAM process node, which allows a 24Gb DRAM die to be assembled into an 8-high cube within an industry-standard package dimension. 

What is more interesting is that Micron will also be working on an upgrade – a 12-high stack with 36GB capacity – which will begin sampling in the first quarter of calendar 2024. Vaidyanathan said Micron provides 50% more capacity for a given stack height than existing competitive solutions. 

Micron noted that it developed this “breakthrough product” by leveraging its global engineering organization, with design and process development in the United States, memory fabrication in Japan, and advanced packaging in Taiwan. When asked if the products, once ready in early 2024, will be made available for its clients in China, Micron did not respond.

The HBM3 Gen2 product development effort will be through a collaboration between Micron and TSMC. “TSMC has received samples of Micron’s HBM3 Gen2 memory and is working closely with Micron for further evaluation and tests,” Micron noted.

Micron’s roadmap

The post Micron Technology will release the most advanced HBM by 2024 to speed up AI training appeared first on Tech Wire Asia.

As a country known for its advanced technology, Japan needs to catch up in the generative artificial intelligence (AI) race. While countries like the US, China, and European Union (EU) are leading with their algorithms, Japan’s lag is attributed mainly to its shortcomings in deep learning and extensive software development.

For starters, the key to generative AI development is large language models, which underpin ChatGPT and Baidu’s Ernie Bot, capable of processing vast data sets to generate text and other content. But as Noriyuki Kojima, co-founder of Japanese LLM startup Kotoba Technology puts it, Japan is trailing behind the US, China, and the EU in developing those algorithms.

“Japan’s trailing position in the field of generative AI largely stems from its comparative shortcomings in deep learning and more extensive software development,” Kojima said in an interview with CNBC. To put into context, based on a report by Reuters in May, citing research from a consortium of state-run institutes, Chinese organizations, including tech giants Alibaba and Tencent, have launched at least 79 LLMs domestically over the past three years.

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In the US, corporate powerhouses such as OpenAI, Microsoft, Google, and Meta play a significant role in propelling the country’s LLM advancements. But deep learning requires a “robust community of software engineers” to develop necessary infrastructure and applications, Kojima added. 

Japan, however, will face a deficit of 789,000 software engineers by 2030, according to the country’s Ministry of Economy, Trade and Industry (METI). Japan is also facing hardware challenges since LLMs need to be trained using AI supercomputers like IBM’s Vela and Microsoft’s Azure-hosted system. Unfortunately, Nikkei Asia reported that no private Japanese company possesses its own “world-class machine” with those capabilities.

How can Japan catch up on gen AI?

Kojima told CNBC how government-controlled supercomputers like Fugaku “hold the key” to Japan’s pursuit of LLMs. “Access to such large-scale supercomputers forms the backbone of LLM development, as it has traditionally been the most significant bottleneck in the process,” he explained.

Japan’s Fugaku supercomputer at the Riken Center for Computational Science in Kobe, Hyogo prefecture. The Fugaku supercomputer, built with government backing and used in the fight against the COVID-19 coronavirus, was ranked as the world’s fastest, based on its developers announcement on June 22, 2020. (Photo by JIJI PRESS / AFP) / Japan OUT

The upside is that the METI has plans to introduce a new cutting-edge supercomputer through its affiliated laboratory to support the development of generative AI in Japan, according to Nikkei’s latest report. According to Nikkei, the plan is for the National Institute of Advanced Industrial Science and Technology (AIST) to develop a supercomputer with a computing capability roughly 2.5 times greater than its existing machine as early as 2024. 

Under METI’s supervision, the research arm will make it available to domestic companies developing generative AI through a cloud service. AIST, one of Japan’s most prominent research institutes, plans to establish a new research center for supercomputers and quantum technologies in July. METI will provide 32 billion yen (US$226 million) to finance the installation of facilities.

Nikkei also stated that AIST will operate the new supercomputer at the center for its studies on quantum technology and open it to Japanese companies. So far, AIST’s existing supercomputer is accessible from outside via a cloud service.

Separately, the Tokyo Institute of Technology and Tohoku University also have plans to work on the country’s LLMSs prowess. Based on an announcement in May, the institute and university plan to use the Fugaku supercomputer to develop LLMs based primarily on Japanese data in collaboration with the supercomputer’s developers, Fujitsu and Riken. 

The organizations also plan to publish their research results in the same year AIST intends to release its supercomputer, which is 2024. According to Fujitsu, the research results would help other Japanese researchers and engineers develop LLMs.

Above all the efforts mentioned above, the Japanese government also plans on investing 6.8 billion yen (US$48.2 million), about half the total cost, to build a new supercomputer in Hokkaido that will begin service as early as 2024, Nikkei reported. That particular supercomputer, built in Hokkaido, will specialize in LLM training to promote Japan’s development of generative AI.

Based on reports, the cloud service provider Sakura Internet is assembling the supercomputer in Ishikari City. It’ll contain more than 2,000 graphics processing units from Nvidia, which are necessary for LLMs. Interestingly, the Sakura Internet’s supercomputer would be able to develop GPT-3 within roughly three days and about a year to develop GPT-4.

The post Japan to catch up on gen AI with a new supercomputer appeared first on Tech Wire Asia.

When it comes to autonomous vehicles and the technology that goes behind them, China has been at the forefront in recent years. As US policymakers become more focused on espionage and surveillance, Chinese companies testing in California–the world’s largest autonomous vehicle test bed–may be on borrowed time.

The most recent indication is from US Transportation Secretary Pete Buttigieg, who told Reuters on July 20 that his agency has national security concerns about Chinese autonomous vehicle companies in the American market after some lawmakers called for restrictions on their operations. 

First and foremost, the prominence of Chinese self-driving vehicles in the US can be based on the California Department of Motor Vehicles’ yearly report on autonomous vehicle testing in the state, released on February 17. Data showed that Chinese firms will continue to test their vehicles in the state throughout 2022, even as some policymakers voice concerns over national security risks.

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Chinese firms, including Baidu’s Apollo, Pony.ai, AutoX, WeRide, Didi Chuxing, Qcraft, and Deeproute.ai, tested their autonomous vehicles in California last year, collectively racking up over 450,000 miles in test miles driven during the period covered by the report (December 2021 through November 2022). Pony.ai led the pack of Chinese firms, with its test vehicles going 280,413 miles.

Source: California Dept of Motor Vehicles

It is worth noting that Chinese AV makers didn’t quite match the level of testing they conducted in 2021 when they drove test vehicles over 510,000 miles around California. Although most AV makers decreased their testing in California from the previous year, WeRide and Apollo surpassed their testing numbers in 2021.

Chinese self-driving startup WeRide branded autonomous vehicles seen undergoing testing at the company’s San Jose corporate campus in the Silicon Valley, California.

Chinese autonomous vehicle companies like Inceptio, NIO, and XPeng have permits to test in the state, but the records show that they conducted no test drives last year.

What is the issue with Chinese autonomous vehicles in the States?

The fear of autonomous vehicles being turned into “weapons” goes back to 2017, when self-driving technology was seen advancing faster than regulators could keep up with. Since then, regional and national governments have been grappling with the issue of when to allow autonomous cars onto their roads and under what conditions.

By late last year, lawmakers were concerned about a flood of data-hungry cars from China taking over American streets. In a letter to the US National Highway Traffic Safety Administration dated November 18, 2022, Representative August Pfluger asked whether Washington is prepared for the security threat posed by the coming influx of Chinese-made intelligent and autonomous vehicles.

 “I remain concerned that a lack of US oversight in AV technology has opened the door for a foreign nation to spy on American soil, as Chinese companies potentially transfer critical data to the People’s Republic of China,” Pfluger writes. He asked the regulator to explain how it has vetted the national security risk posed by these Chinese companies.

Fast forward to this year, in another July 17 letter to Buttigieg and Secretary of Commerce Gina Raimondo, a bipartisan group of four lawmakers asked for an investigation into the prevalence of Chinese AV technology in the US and how it can be restricted. 

“Technology used by AVs, LiDAR, RADAR, cameras, AI, and other advanced sensors and semiconductors can all be used to collect data on the American people and infrastructure that could be shared back to China and ultimately to the Chinese Communist Party (CCP),” the lawmakers wrote. As with the November 2022 letter, the lawmakers in this July letter shared similar concerns on autonomous vehicles and equipment testing in the US.

“Whether we are talking about hardware or software, in the same way, there are concerns around telecom or TikTok, there are concerns around transportation technologies,” Buttigieg told Reuters in response to the letter he received. 

What’s next for Chinese AV firms in the US?

Most US lawmakers argue that China could use autonomous and connected vehicles as a pathway to incorporate their systems and technology into America’s infrastructure. So far, the US and most of its allies have already banned Chinese corporate giant Huawei from building 5G infrastructure. So, the next frontier of the US-China war is most certainly the “next-generation” vehicles.

Frankly, the US is not alone in its fears. China themselves had that anxiety about American-made smart and electric vehicles. Earlier last year, for example, Beijing placed firm restrictions on where Teslas could drive, particularly around military installations, amid high-level Communist Party meetings.

Chinese companies with autonomous vehicles are still relatively unrestricted in California and are still taking full advantage of their testing permits. But, given the growing unease over Chinese espionage, the coming years could be difficult for China’s AV companies in the US.

The post Chinese autonomous vehicles in the US may soon be under scrutiny appeared first on Tech Wire Asia.

Automation in the manufacturing sector has revolutionized the way products are made, from the earliest days of mechanization to the advanced robotics and machine learning technologies we have today. However, many businesses are still grappling with their automation efforts. In a 2022 report by IDC on IT Operation Challenges in the Asia Pacific, automation was identified as the biggest concern by enterprises.

The report stated that 14.6% of businesses needed more automation with pre-built software and could not set up pre-emption and auto-remediation. The upside is that regional enterprises are looking to scale their automation initiatives, and the voracious appetite is driving automation across diverse horizontal and industry-specific business processes.

That is also why automation has steadily risen to the top of enterprise priorities over the last few years. But many business and IT leaders need help with a juggling act: build the new while keeping the old running smoothly and reliably. What’s making this juggling act even more difficult? Being stuck with the complexity and technical debt that comes with legacy middleware technology.

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Tech Wire Asia had an opportunity recently to discuss with an executive from Boomi, a leading provider of cloud integration and workflow automation solutions, on how automation can help businesses thrive and remain agile in the face of current disruptions. David Irecki, Boomi’s Director for Solutions Consulting, also discussed why legacy application modernization is a game-changer for manufacturing.

This interview has been edited for length and clarity.

TWA: How has Boomi evolved since it first entered the market?

David Irecki, Boomi’s Director for Solutions Consulting,

Boomi is an integration platform, also known as an IPaaS, that is based in the cloud. Our platform basically allows companies to integrate applications, databases, partners, and customers easily. We’ve been in the market for over 20 years and have over 20,000 customers, the largest of the integration vendors out there. 

For us, it’s been a journey and an evolution with our customers, not just in manufacturing, but we have customers all over the spectrum, from education to FSI to retail. Integration is ubiquitous; every company needs it, whether they know it or not.

TWA: In terms of automation, how would you describe the changes within the manufacturing sector over the years?

For Asia Pacific, it is not just with manufacturing. Many industries are still undergoing significant transformation due to the remnants of Covid-19, changes in the global economy, or technological advances. If we look at manufacturing specifically, they are an example of legacy modernization. 

Many organizations still have legacy on-premise applications and may still have data on mainframes. So more of them are looking at how they modernize those systems, more so around ERP modernization, and how they move data between those systems as they update. That’s a massive thing for us. 

The automation side of the coin plays a significant factor as well. Take B2B, for instance – a lot of the process is manual, like the processing of invoices and orders via email, FTP, or other means. 

Many customers are coming to understand how we can automate those processes. But in general, there is quite a broad spectrum of use cases, depending on the company’s maturity or even the country.

TWA: Regarding integrating applications and data in the enterprise, what issues do companies need help with today?

Yes, many companies saw legacy systems and processes as a barrier, but part of that was also data silos. Data is an essential part of a business because if you can receive and analyze that data, you can translate it into precious business intelligence. 

IDC Reveals Asia Pacific Enterprises’ Top 7 IT Operations Challenges With Their Existing Operations Management Software – 2022 Jul -F-1

But data silos, which are essentially a collection of data held by one group that is not quickly or thoroughly accessible by other groups in the same organizations, lead to several processes. After all, there is a cost to using data, and behind the glamor of powerful analytical insights is a backlog of tedious data preparation.

It’s not only about breaking down those data silos but also about the collaboration between teams since many do not communicate with each other. Then, move with the goal of integration in mind. Each progressive step should also build toward an integrated platform for your enterprise data. You don’t want to recreate a whole new set of silos, albeit with advanced capabilities.

To do this, you’ll need support from the highest level. Getting the buy-in of those stakeholders is critical because they control the budget, understand the internal politics, and can facilitate the necessary. After all, organizational changes will be inevitable as you progress in using data in operational and strategic applications.

TWA: How does implementing modern tech help manufacturers leverage data analytics to improve operational efficiency for their logistics? 

The majority of these businesses are trying to respond to increasing customer demands. And they’re trying to do that faster while optimizing those processes and trimming costs. Whether they’re looking at legacy modernization or supply chain efficiency, or supporting B2B channels, they’re looking at different ways that technology can help them achieve those outcomes quicker and less costly. 

And in doing so, it opens a lot of new opportunities for the business. Especially with many of our customers when they use tools to analyze their data. Allowing a single, 360-degree view makes it easier for companies to gather those insights and share that data up and down the supply chain,

TWA: How can integration platforms help the manufacturing sector remain resilient and thrive amid disruptions and uncertainty?

Many countries in Asia do not solely rely on local talent but also foreign talent as part of their businesses. When their foreign town talent left over the Covid-19 period, it’s been tough to plug those gaps again. 

And that’s where an integration platform can automate some of those processes that were done by manual labor in the past, so you can fill those workforce gaps with automation. That’s one way that has really stuck out with me recently.

TWA: The costs and failures of previous attempts at big-bang modernization approaches are well-known. How can a company overcome such obstacles?

I always say start small. We see plenty of use cases where businesses that create small prove out the return, and then very quickly, the company grows from there.

TWA: When it comes to integrating applications and data in the enterprise, what are the issues companies are struggling with today? How do you recommend they address these challenges?

Data is always a prevailing theme. We did some research a couple of years ago within our customer base and understood that over 60% of data was dark within an organization. In the first place, you need to know what you’re integrating and if it is the correct data. 

That is a problem where technology like data catalogs can assist because you can catalog that data, understand it, and potentially use data preparation tools to clean it up. The next is around connectivity. Yes, and so many vendors like ourselves, we have a wide variety of random connectors and too many applications for your Salesforce, SAP, NetSuite, etc. 

That is where we have taken on the task of abstracting and, from a friendly, simple user interface, how to connect to that application. But it’s a familiar story across all of our customers. It’s all about understanding your data, relating to it, and then understanding your use case. 

Is it complex enough that you require central IT to do it? We have more and more customers that are looking at the low code capabilities of Boomi. For example, if we use Workday in HR, and a Workday consultant understands integration, why can’t we provide them access to the Boomi platform and let them build the HR onboarding use case and leave them from central IT?

Any near/long-term outlook regarding integration and automation for enterprise?

The low code approach to integration continues to resonate in the market. A recent IDC report said that by 2024, 50% of enterprises will be training and supporting business users to automate their processes using low-code tools. So that resonates, and the skill shortage will still be part of that. 

Automating and integrating data will be seen as a means to increase productivity, even though you don’t have that labor. I also think the democratization of integration and automation across the business will also be critical. Because by offering data to other parts of the company or offering integration processes, they can build things they never thought they’d be able to before. IDC has said that they would see a 20% revenue increase. 

The post Boomi: How automation helps the manufacturing sector thrive in adversity appeared first on Tech Wire Asia.

Even governments, well-known as slow-moving on the technology uptake, are realising that open financial data helps drive economic growth. McKinsey & Associates states, “Analysis suggests that the boost to the economy from broad adoption of open-data ecosystems could range from about 1 to 1.5 percent of GDP in 2030,” and, “research suggests that more than half the potential value remains inaccessible, particularly the value that financial institutions could gain directly through greater efficiency and reduced fraud costs.”

But as the amount of data available grows hugely, the edge gets harder to find, because value gets harder to see in a data-blizzard. Even when big FSI companies invest heavily in technological solutions like data lakes, simply capturing data doesn’t give you the edge. It doesn’t turn data into actionable value.

That means you can frequently end up paying through the nose for technology that, while it’s nice to have, is of no little practical value to your day-to-day financial decisions. There’s undoubtedly valuable material in there somewhere – but it would take you longer to go through the massed data to find it than it would ultimately be worth.

Focusing the information firehose

That’s where companies like Refinitiv come in – providing a combination of contextualized insight for the decisions you need to make day-to-day, and the most useful, valuable data as and when you need it to guide your decisions towards profitability. Whatever your role in the FSI, getting that kind of quality data, targeted towards the needs you have, and in the time frame you want, can help cut through the data-blizzard and give you back the edge you need. As data lakes become stagnant, curated data streams stir the waters.

We spoke to Matt Eddy, Head of Real-Time Customer Managed Services at Refinitiv, an LSEG business, about how the company adds value to companies in the FSI, even if they have their own data lake.

“A lot of our customers across the FSI have absolutely invested heavily in data lakes. Have they done it right? Possibly… possibly not. And, if you get it wrong, that’s when your data lake becomes a data swamp. If you haven’t properly governed your data inputs, you end up with lots of data of minimal actual use. In a lot of these companies, their data lake volumes aren’t shrinking, they don’t want to throw data away.”

Refinitiv’s mission is, he said, “To simplify and standardize companies’ relationship to data.”

Source: Shutterstock

What that means is that Refinitiv is ‘slicing and dicing’ through the available data, to feed only relevant, pre-normalised, value-added material to screens across the FSI – without bringing spurious information that needs to be parsed again by in-house systems.

Developing a method of parsing data, or having it curated before consumption, is becoming hugely important. In 2021, a survey of financial professionals commissioned by Google Cloud found that 93 percent of exchanges, trading systems and data providers offered cloud-based data and services and that all planned to offer new cloud services that included derived data. Data ingestion and use is, it’s said, more common on the buy-side, but it’s still a huge influence.

But there’s more to the process of sharpening the data edge than mere access. The point about having accurate, rapid access to FSI data that helps you make better decisions is that you want it with as little fuss as possible. But translating that into a customer experience (end-user experience, trader experience, et al.) has been tricky in the past, because in less tech-enabled days, there has been one way to do things – whether or not one size fitted all.

DIY – if you want to

Refinitiv has a range of access methods, that allow its clients flexibility not only with their payment model, but also with their access and support needs.

“As an example, with Real-Time – Optimized, which is our AWS-hosted feed, we’re rolling out a self-serve self-authentication, self-entitlement process,” Matt explained. “The idea is that once our clients have purchased the product, they’ll get access to a portal, they’ll create their own ID, add and remove permissions as they need and be able to monitor and manage that connection without any kind of engagement if they so wish. We’re also taking that one step further and looking to roll it into ecommerce channels as well.”

In other words, if you want to define the parameters of the data you get without being handheld through an induction process, you’re free to do that. But Matt was also keen to say that for those who wanted old-style, end-of-the-line customer support and guidance, that remains available too. The point is that the FSI, like the rest of the tech-enabled world, is changing – and Refinitiv aims to be there for both early adopters, leave-me-be’ers, and the “What does this button do?” crew who quite like their hand held when the way they get their daily data changes for the better.

Source: Shutterstock

When it comes to value-added FSI data, the chances are high that companies will want access to everything there is, so that staff with different priorities can shape their own feeds appropriately for their own use, but just as it’s trying to cover the waterfront in terms of user support, the same is true of its pricing models.

Resonating with cell phone contracts, with Refinitiv’s data services, they aim to offer the option for customers to either buy the credits they think they’ll need for a given period of time, and then stay within those limits, or they can buy a kind of “all you can eat” package, and access any available data however many times they like, as long as their contract runs.

Whether you need strong, dependable data on the latest trends, or proven analytics to help you see the market more clearly in a given light, companies like Refinitiv take you through the data-blizzard and remind you what’s actually worth knowing for your bottom line. Companies like Refinitiv are focused on finding a cure-all for the snow-blindness that too much data can bring.

They’re a way of helping companies re-sharpen their edge in the financial services industry. And they’re continually evolving to meet that industry’s increasingly complex needs.

For more info on how Refinitiv can offer you real-time data insights, click here.

The post The data you need to sharpen your edge – Refinitiv appeared first on Tech Wire Asia.

A month ago, IDC forecasted global spending on AI, including software, hardware, and services for AI-centric systems, to reach US$154 billion this year, an increase of 26.9% over the amount spent in 2022. The outlook suggests that more large and small companies are using their trove of data and integrating their systems with AI to avoid lagging behind competitors.

Why wouldn’t they? After all, data and AI have been having a profound impact on the way businesses have been operating in recent years. Companies are adopting AI to augment human abilities, automate repetitive tasks, provide personalized recommendations, and make data-driven decisions with speed and accuracy.

SMART CYBERSECURITY SUMMIT SINGAPORE: FUTURE SKILLS, BURNOUT AND THE BOARDROOM

Dashveenjit Kaur | 19 April, 2023

C-Levels and critical decision-makers are gathering later this week at the inaugural Data & AI Leaders’ Summit in Singapore as the city island aims to become a big data hub and world leader in AI adoption. 

With complimentary tickets, the Summit will take place on Thursday, April 27, at Marina Bay Sands, offering attendees an opportunity to network with professionals from the field.

Recent statistics reveal that almost 70% of SMEs in Singapore are yet to take advantage of data analytics. The country scored only 49.7 out of 100 for business readiness in a Statista AI readiness index. That said, the summit, both an exhibition and conference, is tailored for C-level executives, senior decision-makers, and technology end-users. 

There would be 25 insightful sessions by over 30 leaders from the industry in the region, covering critical topics such as chatbots, generative and conversational artificial intelligence like ChatGPT, governance and ethics, data management, strategy, and sovereignty. As harnessing new technologies to innovate businesses efficiencies is the top priority amongst enterprise leaders, the generative AI track will explore the applications of this technology, especially in the processing and understanding of large amounts of text, generation of text based on input, and the abilities to provide accurate and informative responses to a wide range of topics. 

Data sovereignty discussions will address why data privacy protection is more important than ever, and companies must ensure their customers and employees’ sensitive data is safe wherever that data is stored, shared and used. The adoptive AI track will provide guidance on how adaptive systems will enable new ways of doing business.

Among the speakers that will be present at the Summit includes Geetha Gopal, Head of Infrastructure Projects, Delivery and Digital Transformation at Panasonic Asia Pacific, Sachin Tonk, Deputy Chief Data Officer at GovTech, and Samuel Chu, Head of Data Science and Analytics for Singlife with Aviva.

The exhibition floor, on the other hand, will showcase cutting-edge solutions from Aerospike, Alteryx, Azul, Collibra, Confluent, Fivetran, and Juniper Networks. Tech Wire Asia is delighted to be an official media partner for Asia’s premier leadership summit. 

The post What to look forward to at the Data & AI Leaders’ Summit in Singapore this week appeared first on Tech Wire Asia.

Edge-as-a-Service enables organizations to have access to networks, operations and edge computing for real-time automation and processing. For most organizations today, processing data at the edge would mean better insights, especially when it comes to meeting customer demands.

Simply put, with Edge-as-a-Service, SaaS providers gain access instantly to an edge deployment model for their solutions. This means they do not need to build or manage their own edge network. However, there are some challenges to Edge-as-a-Service. The biggest one would be linked to network coverage. As it collects data and produces insights in real-time, the network for Edge-as-a-Service needs to be the strongest.

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And when it comes to the network, the main challenge would be providing seamless coverage, especially in areas that are away from fixed terrestrial networks. Apart from that, Edge-as-a-Service could also experience surges that require the network to support its demand.

As such, NTT, a provider of Edge-as-a-Service to enterprises, has announced a multi-year partnership with SES, a leading global content connectivity service provider via satellite. The partnership will bring together NTT’s expertise in networking and enterprise-managed services with SES’s unique satellite capabilities to deliver reliable connectivity to enterprises.

Utilizing SES’s O3b mPOWER and NTT’s fully managed Private 5G and Edge Compute, the solution is intended for companies operating in regions where terrestrial networks are lacking and enterprises wanting to leverage high-performance connectivity to increase their efficiency and grow revenue. Through the combined versatility of Private 5G networks and satellite technology, this end-to-end solution is expected to propel industries – such as energy, mining, maritime, manufacturing, industrial, etc. – that have otherwise been limited by connectivity today and will need to ramp up their digital transformation plans and increase revenue streams.

According to Miriam Murphy, CEO of Europe at NTT Ltd, as organizations grapple with the challenges of a rapidly changing world, it is now more important than ever to leverage the power of technology to drive growth and innovation.

For John-Paul Hemingway, Chief Strategy Officer at SES, the partnership is one of its kind as both companies jointly provide comprehensive and resilient connectivity solutions for customers around the world.

“In addition to its predictable low latency capabilities, O3b mPOWER’s best throughput and full flexibility on asymmetric or symmetric services will result in the seamless integration and extension of terrestrial and satellite networks, enabling our customers to unlock the full potential of emerging technologies like 5G, IoT, and cloud computing, and drive digital transformation across industries,” he said.

(Source – NTT)

Perfecting Private 5G and Edge-as-a-Service

The joint solution will deliver coverage to over 190 countries with public-private roaming. In addition to NTT’s Private 5G and Edge Compute capabilities, NTT will also provide use-case consulting and design, application development, system integration, implementation, and managed services, while SES will provide end-to-end satellite networks via O3b mPOWER that will be seamlessly integrated with NTT’s offering.

Olivier Posty, Country Managing Director Luxembourg, NTT Ltd explained that private 5G is a transformative power that enables enterprises to build upon existing network infrastructure and deliver reliable, high bandwidth, and low latency connections for multiple use cases operating on a single Private 5G network.

“As our customers continue to innovate, network partners with the right skills and expertise will be critical to success in today’s competitive market. NTT’s robust Private 5G network-as-a-service full-stack solution, delivered on-premises, at the edge, or as a cloud service, is complemented by NTT’s 24/7 remote monitoring services and a CIO self-service portal, ensuring that NTT’s full stack of managed Edge Compute services delivers real-time actionable intelligence to drive processing efficiency and accelerate business performance,” added Posty.

NTT’s Edge-as-a-Service offering includes IoT, Edge Compute, and Private 5G connectivity delivered by NTT across its global footprint. NTT’s Edge-as-a-Service is a unique, fully managed, integrated solution that accelerates business process automation, enabling enterprises to quickly deploy their applications more securely and monitor them closer to the edge, thereby reducing downtime, improving user experience, and optimizing costs.

The partnership between NTT and SES comes as organizations are increasingly turning to technology to drive growth and innovation. Organizations recognize the positive impact of high-speed connectivity and resilient networks on business operations, driving demand and fueling widespread digital transformation. By leveraging their respective strengths, NTT and SES are well-positioned to provide customers with the innovative Edge-as-a-Service solutions they need to succeed in a rapidly changing world.

The post NTT partners SES to improve Edge-as-a-Service and Private 5G offering appeared first on Tech Wire Asia.