Southeast Asian Business Leaders Must Step Up On Development

Consider two statistics about Indonesia: Economists forecast the country will become the world’s fourth-largest economy by 2050. We also have the world’s highest burden of tuberculosis after India, claiming the lives of 150,000 to 200,000 people every year.

These figures illustrate the extreme inequalities dogging the world’s fourth-most populous nation, despite impressive economic growth in the last decade and cutting poverty by half.

In Jakarta and other main cities, a burgeoning middle class is drawing local and international investors, from vehicle companies to financial services to digital technology to retail and fast food chains. Yet tuberculosis still affects far too many people, particularly poor people suffering from malnutrition, while malaria remains a major problem in the remote, heavily forested province of Papua in eastern Indonesia.

To achieve its full potential, Indonesia needs to tackle inequality by investing more in its people. According to the World Bank, growth has primarily benefited the richest 20% and left the remaining 80% of the population–about 205 million people–behind.

As the Bank’s Human Capital Project points out, education and health are two of the best ways to support prosperity and prepare countries for the economy of the future. With education you can change the fate of a country, but better health is central to human well-being. Healthy people live longer lives, are more productive and save more.

I was born into a working-class family at a time (the 1950s) when most families in Indonesia had no access to healthcare. Thousands of children died each year from preventable diseases such as measles, polio and malaria. My father had a business making pedicabs, while my mother ran a fabric shop in the city. When I became an entrepreneur, I felt compelled to give back to Indonesia. Philanthropy is not about making a donation. It is a commitment related to continuity and sustainability, and requires a well-planned system to have impact.

Since 2015, the Tahir Foundation has partnered with the Bill & Melinda Gates Foundation and the Global Fund to Fight AIDS, Tuberculosis and Malaria, which have played a key role in reversing the course of these epidemics around the word. In Indonesia, the partnership’s efforts are paying off: TB mortality rates have fallen by 44% and TB incidence was down by 14% from 2000 to 2017, thanks to improved case finding and better diagnostics. In 2017, more than half of Indonesia’s districts were officially declared malaria free–a major feat for a diverse archipelago of more than 17,000 islands and more than 300 ethnic groups.

Still, more robust investments are needed. Tuberculosis places a huge social and financial burden on the people who have the disease, as well as on their families and communities. Most of the infections occur in people at their most productive age, draining billions of dollars in loss of productivity due to premature death and medical costs.

I hold the conviction that the private sector and business leaders have an important role to play in public health and development in emerging economies in Southeast Asia, many of which share similar challenges and opportunities. The private sector can bring not only funding, but technical expertise, creativity, and innovation, and are often well positioned to drive policy change.

The government of my country has done a lot for public health, including rolling out a universal health insurance scheme that is designed to provide a wide range of services from maternal care to heart surgery for its entire population by the end of 2019. But the private sector can fill the gaps to complement public resources by expanding access so that all Indonesians benefit from better health.

In 2014, a coalition of Indonesian business leaders, in partnership with the Bill & Melinda Gates Foundation, came together to create the Indonesia Health Fund, a significant step toward making Indonesia self-reliant in health funding and a model for philanthropic collaboration in the region. Over the past four years, the fund has contributed to family planning programs, TB research and advocacy programs, as well as TB screenings

It shows what can happen when public and private sectors come together with a common aim. It is more important than ever with the Global Fund now calling on the world to step up the fight against HIV, TB and malaria in the face of new threats from all three diseases. Raising their target of at least $14 billion will help save 16 million lives over the next three years, avert 234 million new cases and infections, and help us get back on track to end these diseases. The fund is calling on the private sector to contribute at least $1 billion of this total. So let us all do our share.

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Doctor Yulismar checks the condition of a patient who has tuberculosis bacteria at the Indonesian Association Against Tuberculosis (PPTI) clinic in Jakarta, Indonesia, on March 24, 2016. (Photo: Jefri Tarigan/Anadolu Agency/Getty Images)

Disclosure: Dr. Tahir is the owner of the license to publish Forbes Indonesia magazine.

Source: Southeast Asian Business Leaders Must Step Up On Development


AI And The Third Wave Of Silicon Processors


The semiconductor industry is currently caught in the middle of what I call the third great wave of silicon development for processing data. This time, the surge in investment is driven by the rising hype and promising future of artificial intelligence, which relies on machine learning techniques referred to as deep learning.

As a veteran with over 30 years in the chip business, I have seen this kind of cycle play out twice before, but the amount of money being plowed into the deep learning space today is far beyond the amount invested during the other two cycles combined.

The first great wave of silicon processors began with the invention of the microprocessor itself in the early 70s. There are several claimants to the title of the first microprocessor, but by the early 1980s, it was clear that microprocessors were going to be a big business, and almost every major semiconductor company (Intel, TI, Motorola, IBM, National Semiconductor) had jumped into the race, along with a number of hot startups.

These startups (Zilog, MIPS, Sun Microsystems, SPARC, Inmos Transputer) took the new invention in new directions. And while Intel clearly dominated the market with its PC-driven volumes, many players continued to invest heavily well into the 90s.

As the microprocessor wars settled into an Intel-dominated détente (with periodic flare-ups from companies such as IBM, AMD, Motorola, HP and DEC), a new focus for the energy of many of the experienced processor designers looking for a new challenge emerged: 3-D graphics.

The highly visible success of Silicon Graphics, Inc. showed that there was a market for beautifully rendered images on computers. The PC standard evolved to enable the addition of graphics accelerator cards by the early 90s, and when SGI released the OpenGL standard in 1992, a market for independently designed graphics processing units (GPUs) was enabled.

Startups such as Nvidia, Rendition, Raycer Graphics, ArtX and 3dfx took their shots at the business. At the end of the decade, ATI bought ArtX, and the survivors of this second wave of silicon processor development were set. While RISC-based architectures like ARM, MIPS, PowerPC and SPARC persisted (and in ARM’s case, flourished), the action in microprocessors never got back to that of the late 80s and early 90s.

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Competition between Nvidia and ATI (eventually acquired by AMD) drove rapid advances in GPUs, but the barrier to entry for competitors was high enough to scare off most new entrants.

In 2006, Geoffrey Hinton published a paper that described how a long-known technology referred to as neural networks could be improved by adding more layers to the networks.

This discovery changed machine learning into deep learning. In 2009, Andrew Ng, a researcher at Stanford University, published a paper showing how the computing power of GPUs could be used to dramatically accelerate the mathematical calculations required by convolutional neural networks (CNNs).

These discoveries — along with work by people like Yann LeCun and Yoshua Bengio, among many others — put in place the elements required to accelerate the development of deep learning systems: large labeled datasets, high-performance computing, new deep learning algorithms and the infrastructure of the internet to enable large-scale work and sharing of results around the world.

The final ingredient required to launch a thousand (or at least several hundred) businesses was money, which soon started to flow in abundance with venture capital funding for AI companies almost doubling every year from 2012. In parallel, large companies — established semiconductor heavyweights like Intel and Qualcomm and computing companies like Google, Microsoft, Amazon and Baidu — started to invest heavily, both internally and through acquisition.

Over the past couple of years, we have seen the rapid buildup of the third wave of silicon processor development, which has primarily targeted deep learning. A significant difference between this wave of silicon processor development and the first two waves is that the new AI or deep learning processors rarely communicate directly with user software or human interfaces — instead, these processors operate on data.

Given this relative isolation, AI processors are uniquely able to explore radically different and new implementation alternatives that are more difficult to leverage for processors that are constrained by software or GUI compatibility. There are AI processors being built in almost every imaginable way, from building on traditional digital circuits to relying on analog circuits (Mythic, Syntient) to derivatives of existing digital signal processing designs (Cadence, CEVA) and special-purpose optimized circuits for deep learning computations (Intel Nervana, Google TPU, Graphcore).

And one popular chip architecture has been revived by a technology from the 30-year-old Inmos Transputer: systolic processing (Wave Computing, TPU), proving that everything does indeed come back in fashion one day. Think of systolic processing as the bell bottoms of the silicon processor business.

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There are even companies such as Lightmatter looking to use light itself, a concept known as photonic processing, to implement AI chips. The possibilities for fantastic improvements in performance and energy consumption are mind-boggling — if we can get light-based processing to work.

This massive investment in deep learning chips is chasing what looks to be a vast new market. Deep learning will likely be a new, pervasive, “horizontal” technology, one that is used in almost every business and in almost every technology product. There are deep learning processors in some of our smartphones today, and soon they will be in even lower-power wearables like medical devices and headphones.

Deep learning chips will coexist with industry-standard servers in almost every data center, accelerating new AI algorithms every day. Deep learning will be at the core of the new superchips that will enable truly autonomous driving vehicles in the not-too-distant future. And, on top of all of this silicon, countless software offerings will compete to establish themselves as the new Microsoft, Google or Baidu of the deep learning future.

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By: Ty Garibay

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