A recent report stated that a survey conducted by an Information and Technology market research and advisory firm highlights that AI adoption in the ASEAN region is on the rise.

Current AI adoption rates stand at 14% across Southeast Asia as compared to just 8% last year, marking a clear move by companies to embed some form of AI/cognitive intelligence into their operations.

Discovery of better business insights has become the most important adoption driver according to more than half (52%) of respondents, moving from third most important in 2017, revealing a maturity in the way the region is harnessing AI to enhance their business. Other top drivers this year are enhanced process automation (51%) and improved productivity (42%).

Malaysia came in fourth with 8.1% of all its organisations being open to the adoption and integration of Artificial Intelligence into their operation. It followed behind Indonesia (24.6%), which was the country that led the pack in terms of adoption. Next were Thailand (17.1%), Singapore (9.9%).

The top use cases in Southeast Asia include algorithmic market forecasting (17%), and automated asset and infrastructure management (11%).

Experts argue that there are clear opportunities for more organisations in Southeast Asia to leverage AI to create differentiating value. This is particularly because of its pre-existing positive impact already visible across banking, manufacturing, healthcare, and government.

It is expected that investments in AI will continue to rise, as more organisations begin to understand the benefits of embedding AI into their business and how data and analytics can help uncover new insights.

Organisations that do not incorporate AI into their business operations will lose out to their AI-enabled peers who will benefit from the greater predictability, efficiency, and innovation that advanced analytics can bring.

Despite the rise in adoption, organisations in the region are trailing behind those in North Asian countries, in terms of making AI a strategic agenda.

For example, more than 80% of companies in China and South Korea believe AI capabilities will be critical for organisations’ success and competitiveness in the coming years, compared to less than 40% of companies in Singapore and Malaysia.

Lack of skills & knowledge (23%) and the high cost of solutioning (23%) are among the most frequent barriers to adoption named by survey respondents.

While the overall adoption in Southeast Asia falls behind Asia/Pacific (excluding Japan), there are signs to suggest organisations in the region will catch up quickly.

For example, 34% of organisations in Malaysia have plans to adopt AI within two years, the 2nd highest among Asia/Pacific countries.

In solidifying their strategy to turn AI into a differentiator for the business, companies find data from sales, commerce, and marketing to be the readiest, followed by that from customer service & support operations, and IT, security & risk operations.

For those already embarking on their data-to-insights journey, there are varied challenges across sectors. Organisations in the financial services space face more challenges in data federation and model building, while public sector organisations are hindered by data readiness issues.

With a 32 percentage points jump in planned adoption of AI in two years since 2017, Malaysia’s increasing AI focus can be attributed to greater smart cities initiatives and applications in public safety and intelligent transportation. A lot of these initiatives would need more time to unfold and solidify.

However, many Malaysian organisations have concerns about the cost of solutioning and the quality of the model.

Compared to North Asian economies, Malaysian organisations showed less enthusiasm in having in-house AI capabilities which can hinder their ability to understand AI solutions to strengthen their business.

Nevertheless, more than 32% of companies in Malaysia prioritised speech and image recognition interfaces to improve customer experience and enhance omni-channel know-your-customer.

Organisations in Malaysia are recognising how AI and analytics can help solve complex problems and reveal unique insights, at the scale and speed required for our growing markets.

It is important to note that Malaysian organisations must understand how AI could enhance their current staff and technology to drive improved business outcomes.

In the digital economy, AI and analytics are the drivers of organisational success and companies will need a clear path from data to innovation.

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Artificial Intelligence is a concept that has a long tradition in the field of science fiction, popularized by Hollywood movies and iconic writers such as Isaac Asimov. However, IA has also received increased attention in recent years, following news of progress in the field and the prospect of new and tangible innovation, such as automotive cars. The Internet has played an essential role in these developments, mainly as a platform for AI-enabled services, some with significant implications for the continued development of a reliable Internet.

The ability of machines to exhibit advanced cognitive skills to process natural language, to learn, plan or perceive, makes it possible for new tasks to be performed by intelligent systems, sometimes more successfully than humans. Using AI-driven automation in existing industries, along with the use of AI technologies in new emerging areas, artificial intelligence could considerably boost productivity and economic growth.

The following are three ways in which AI can change the world in the future.

1. The Internet of Things

Have you noticed how computers are getting smaller and, at the same time, smarter? They’re also cheaper. Today, there’s a computer inside anything with an on/off button. All these new smart devices – from toasters to toothbrushes, thermostats, light bulbs and cars – network and communicate with each other, with businesses and with consumers – why wouldn’t your car tell your home you’re coming to your home can tell the oven to preheat to the right temperature for that fish it already knows you just bought because you bought it with your phone, and your phone told it? Behind every device is a client, and the next generation of clients is waiting for a connection and intelligent experience. We mean many things connected: 6 billion things that will require support in the future. Those billions of things connected mean massive volumes of customer data. Companies need to be smart about how they collect, digest and apply that data, which is the soul of the Internet of Things… as long as it can be used correctly and at the service of the customer.

All things connected to the Internet could be the meaning of the Internet of things.

Each thing will have sensors that will collect information from the surrounding environment, as well as receive and send data to other devices or people, which will be useful for making real-time decisions or analyzing historical information.

The dilemma is how to process so much data, how to choose what works and what doesn’t. Artificial intelligence is supposed to help do all this, but can an artificial intelligence generate knowledge for the average human, or will machines create their knowledge that only they can understand?

Besides, it is necessary to see the possibilities that this new knowledge will offer, which of course will not be without risks.

They are a bit crazy dilemmas, but they are appealing to discuss, even if it’s a meeting of friends. Maybe it could be a spark of inspiration to start a science fiction story.

One use case shows the benefit of converging AI, IoT, and Blockchain soon. Imagine wearing a device that monitors your heart rate and exercise level every day. The device contains all of your personal information (identity, including fingerprint and facial recognition; contact information; information about your doctor, medications, allergies, etc.) For security reasons, encrypted biometric functions would be stored in a Blockchain database architecture, without altering the databases that are used for large amounts of data and the regulatory compliance required for the data itself. Your insurer takes your health seriously and continuously monitors the different aspects of your health throughout the day using the wearable devices you have registered with them.

While attending an event, your wearable device reports an abnormal heart rhythm. The IA, through analysis, determines that this abnormality is a precursor to a heart attack. An event is triggered immediately to notify your doctor and provide you with all relevant data for the last few days. Your doctor observes a severe event and sends an ambulance to take you to the hospital.

Its wearable devices indicate its exact location, which is sent to the emergency response team with all the necessary data to attend to it. When you arrive at the hospital, the system linked to your digital currency (say, the bitcoin), which is also used by Blockchain, allows you to log in and pay for a private room.

The AI-activated process provided a fully automated experience that saved his life.

2. Intelligent data and analysis

A considerable gap is occurring between companies and customers. Of all the data that customers create, less than 1% is analyzed. Hence 77% of customers say they do not feel that the companies they interact with have a relationship with them. There are so many ways to read the data and so many conclusions to draw about customer habits and preferences, yet most of those potential insights fall by the wayside because companies do not prioritize the analysis of those data. The new tools reveal useful insights about the customer. These perspectives exist across a spectrum of intelligence: the essential tools require you to extract information from them, while the most intelligent tools deliver information and anticipate what you want to know. For the latter, we use automatic learning.

Without a doubt, the future of artificial intelligence lies in the Big Data. A few years ago artificial intelligence was considered a remote possibility, but it is now a reality. However, the complexity of the algorithms developed to provide optimal solutions today requires the use of large amounts of data, i.e., Big Data.

The developments and applications that are being created to provide solutions through artificial intelligence and Big Data are significant advances in technology, business progress and improving the quality of life of citizens.

Currently, AI, or artificial intelligence, has been used to develop different solutions and useful applications:

• Consumer behavior

From the management of customer and user data, you can build accurate and complex consumer profiles that help you develop customized and perfectly matched products. Consumers are complex, and decision-making is driven by so many factors that it is extraordinarily challenging to understand which new products and services will fit their unconscious tastes and needs.

Traditional market studies always revealed the difficulty of understanding the real impact of launching something new onto the market. This large data management would be applied to determine more precise market segments, as well as to predict increasingly complex and demanding consumer behavior more accurately.

• Energy Saving

Solutions are currently being developed to optimize energy consumption in factories, office buildings, shopping centers and even family homes. Through Big Data and IoT (intelligence of things), the buildings themselves can self-regulate the consumption of resources, such as electricity, gas or water, and optimize the expenditure, so that it represents both savings for the owner and a benefit for the environment.

• Safety and security

Controlling crime levels and ensuring security for citizens is a reality when it comes to integrating technology that detects incidents and alerts qualified personnel to respond quickly and effectively. By combining this data management technology with AI, audio and video information from different parts of a city can be obtained and interpreted.

Likewise, there are different applications derived from the use of these technologies that have served to improve security in different environments. For example, companies are already marketing security systems for doors in public or private spaces. There are also cybersecurity solutions to improve IT security in banks, companies and even public entities.

• Customer service

The precise and fast handling of this technology ensures an adequate response in real time for consumers, which has a very positive impact on their satisfaction and brand image. The best known practical example at present is the use of chatbots in the customer service areas.

• Quality of life

The applications that can be created from artificial intelligence devices can solve the life of thousands of people who suffer from some physical or mental disability. From the development of body parts that improve the user’s level of independence to home applications that make life easier for older people. There is a whole range of possibilities for innovative products.

Artificial intelligence and Big Data, inseparable partners.

In short, the future of artificial intelligence lies in the Big Data. These elements are increasingly used and will undoubtedly favor the creation and development of new solutions, tools and applications that will improve the quality of life of users, the efficiency of companies and even the urban and natural environment.

The future lies in using large amounts of data

In that future journey, the AI will have to use large amounts of data to train the algorithms. “The IA is completely dependent on good data,” says Angela Shen-Hsieh. Through customer data, it is possible to build profiles that help optimize products through a customized experience. Many businesses “are not able to use AI because they don’t have access to much information they would like, and they just guess, assume and extrapolate the data without knowing why.”

The AI is going to help us figure out what the data is for

Many companies have been devoting large amounts of money to big data and have not yet been able to make a profit. Because there has been a disconnect between senior management and the technology areas of the companies on what to do with the data, in addition to which we were still at an early stage of the learning curve and the tools were underdeveloped. However, this has changed. Many companies are rethinking their data strategy, have become more practical and are beginning to ask themselves the right question: how can I make my processes more efficient and what do I need to automate data collection? In addition to the appearance of new tools and developments much simpler and more effective shortly.

Big Data and AI have brought about many changes both in the business world and in scientific research. Moreover, over the next few years, these changes will accelerate as more companies and organizations embrace business intelligence and predictive analytics. Big Data can not only analyze shopping habits or communications on social networks, but it will also transform the interaction between people.

How is this change going to happen? 10 predictions of Big Data

1.- The growth of information is unstoppable: it was evident that the size of Big Data will continue to grow. With a simple extrapolation of the amount of data generated over the last 10 years, you can give us an idea of what it will be like in the future. The larger the data size, the more extensive and more powerful the analysis.

2.- The tools will improve: again, something distinct. The tools we use to analyze our data will be sophisticated and, at the same time, simplified. The big technology companies are betting on Big Data and artificial intelligence, and their products are in constant development to achieve a dominant position in the market.

3.- Analytics will be more straightforward: Big Data management will continue to be in the hands of the data scientist, but analysis will become more comfortable and more natural. With more powerful and self-manageable tools. Data analytics will become a capability to be developed by each department’s staff, without having to depend on the IT department.

4.- Analytics will be integrated into necessary business software: As with many other technologies, and as is already happening in large companies, Business Intelligence tools will be incorporated into the underlying business management package.

5.- We will see more nonsense: as the Big Data “democratizes,” the margin of error produced by all those people who, without previous knowledge, begin to use powerful predictive analytics tools will also increase. Without the correct implementation of a business analytics plan and the expert knowledge of a data scientist, you run the risk of producing “wild” conclusions that can ruin our business.

6.- Computers will not only collect data: as research in Artificial Intelligence improves, computers will gain prominence in predictive analytics. They will discover connections between data that we were unable to imagine. Moreover, as these connections between variables become more complicated, it will become more complicated for us to discover how the computer came to that conclusion

7.- It will affect privacy: As the analysis teams can process, interconnect and interpret more data about our behavior and communications, they will know us better, they will discover our habits and propensities. There will undoubtedly be a debate about the limits of Big Data and individual privacy.

Demand for data communication experts will increase: The ultimate goal of Big Data analysis is to present and explain the findings in a clear and straightforward way. This will involve a division of roles in the analytics department: the analysis team, led by a data scientist, and the findings team.

9.- The algorithms will enter the market: as happens as a mature market matures, the “custom solutions” will be abandoned and the “prêt a porter” of the customizable algorithms will be developed. Organizations will buy only the solutions they need and adapt them to their database sources.

10.- Completely new areas will be opened up: right now it sounds like science fiction, but data will be obtained on areas where it was never thought they would be accessible; will we be able to obtain data on street shopping, the analysis of human behavior through its movements captured by video cameras? The possibilities are endless.

3. Automatic learning

With automatic learning, computer systems can take all that customer data and complement it, to operate not only with what has been programmed but also to adapt to changes. The algorithms adapt to the data, allowing them to develop previously unscheduled behaviors. Learning to read and recognize context means that a digital assistant could scan emails and extract what you need to know. Inherent in that learning is the ability to make predictions about future behavior, know the client more intimately, and be no more proactive, even prescriptive.

A couple of decades ago, talking about Artificial Intelligence and Automatic Learning was primarily limited to the world of Science Fiction and fantasy. However, the idea that they can help human beings has been a stimulating and innovative idea that has been developed since the 40s and that even though for some philosophers the belief that the human mind is delimited through mathematical calculations is something unheard of and risky, in 2018 things are painted in a slightly different way.

Today, the impact of Artificial Intelligence and Automatic Learning as powerful online tools gives us the freedom to assume what their impact will be in the world of marketing and advertising.

The Art of Personalization Through Analysis

One of the most functional and practical benefits that this technology brings us in customizing the customer experience, in other words, the I.A. and Machine Learning (also known as Machine Learning) dares to study and relate your fingerprint, your interests and offer you a variety of content especially for you.

However, what does all this mean? How does it generate content in a personalized way? Very quietly, through the assimilation and segmentation of the data that users generate and are used through multiple algorithms, monitoring the most attractive publications and thus offer them to the people who would be most interested in them, humanizing the brand in the advertising and marketing process, improving the quality outreach to both new and existing audiences.

Innovation is Always the Right Direction

One of the main concerns for most companies around the world is the need to innovate to enjoy the ability to establish a link with their consumers by presenting their products in a current, dynamic and effective way. The search for new tools and new methods will set us apart from the rest of the competition.

Artificial Intelligence and Automatic Learning seek to make an impact that differentiates the way in which marketing and advertising have been used so that users can expect a highly personalized experience, automated and based on the lifestyle of each.

Automatic learning: What is expected of the IA and how it will change our lives

Automatic learning algorithms have become a fundamental element in today’s world.  While these names may sound like science fiction or something that has nothing to do with our everyday lives, the reality is that today’s application interfaces, services, and many other things work thanks to innovative algorithms of artificial intelligence (AI) and automatic learning.  Both are unusually hot topics in the IT world, especially among Silicon Valley companies.  For this reason, it is essential to analyze and study the changes that will result from both the IA itself and its implementation.

Before looking ahead and knowing what the stores will offer us in the future, let’s go back in time to discover the history of artificial intelligence and the main reasons behind its creation. The first steps that allowed the birth of this discipline date back to the 17th century. However, it was in the last century, specifically in 1959, that Arthur Samuel, a pioneer of artificial intelligence, suggested that computers could learn autonomously rather than rely on information provided by programmers to function. This conviction was consolidated over the years. The spread of the Internet and the increasing use of sensors and mobile devices have made it possible to create and aggregate vast amounts of data from which machines can now extract relevant information.

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The continual increase in security threats combined with an overwhelming amount of data and false positives is creating major headaches for IT security teams. Additionally, the cybersecurity industry faces a colossal shortage of talent, making it nearly impossible to stay on top of the latest threats.

Enter artificial intelligence (AI).

According to data from ESG research, 12 percent of enterprise organizations have already deployed AI-based security analytics extensively, while another 27 percent have deployed AI-based security analytics on a more limited basis.

The relationship between AI and Machine Learning (ML) is often poorly articulated. Artificial Intelligence is simply concerned with causing machines to perform tasks characteristic of human intelligence. And ML is simply a way in which that AI can be achieved. Because ML provides a mechanism of learning where systems do not need to be explicitly programmed, we now have a chance to achieve AI with the enormously wide and high-fidelity data sets on which modern security systems must function if they wish to be effective.

In this sense, AI can simplify the work of the security operations center (SOC) by aiding with the coordination of many different forms of analysis. It can clarify the intelligence landscape and help weed out noise and false positives. It also holds promise to alleviate cybersecurity staffing woes by potentially automating everything. But there’s much to consider before AI can become the cornerstone of your IT security framework.

The Challenges of AI

At the present, the state of the art in AI is all about performing very narrow and specific tasks. However, sophisticated and advanced attacks cross many different surfaces and knowledge areas, some of which are technological and many of which are simply organizational. These require a highly generalized intelligence which is a widely unrealized goal in AI.

For example, one of the most misleading claims in the market is all the hype around AI transforming the way threat actors work. Realistically, AI is not being used much on the offense side for very simple reasons: the most sophisticated attacks are deeply human, working with strong organizational knowledge gained through existing employees, social engineering, rogue actors, etc. When coupled with knowledge of the most effective communication patterns, these human-led attacks are more likely to succeed. And although AI may play a role in automating attacks as well as defense in the future, most major risks will come from a non-AI approach. AI is simply not yet advanced enough, nor does it have easy access to all the required data, to outperform humans on this front.

On the defensive side, AI has become a marketing buzzword—often used interchangeably with ML—causing considerable confusion, especially in early adoption.

How AI Helps IT Security and Thwarts Attacks 

Although we’re nowhere close to the point the point where AI solutions have total autonomy and can replace highly-skilled security staff, there are aspects of AI and ML that can be used to help enhance the humans who use this technology. For example, the same ESG study notes that 29 percent of respondents indicated that they were interested in using AI-based cybersecurity to accelerate detection—curating, correlating and enriching security alerts, to create a more complete detection story across various expert systems. Additionally, 27 percent see value in using AI-based cybersecurity technology to improve and speed up incident response—prioritizing serious incidents and even automating remediation tasks.

Another significant role for AI in security is to advance threat research. Intelligence is still largely a human research effort. It combines knowledge of current threat actors, tactics, techniques and procedures. It is coupled with a sense for how attacks can leverage vulnerabilities and work across numerous surfaces and is ideally augmented with information sharing within working groups. AI can play a very serious role in accelerating research, automatically generating new indicators of compromise, and identifying future research opportunities. But only if it has the data.

So at the end of the day, AI really is not about replacing humans. It’s about serving them better and helping them focus on the things at which they are best: being creative, executing on high-level reasoning, managing for context, adapting quickly, and sorting through what does and does not matter. Machines are great at speed, repetition, automation and scale: things for which humans would be really inefficient.

Therefore, when it comes to AI, truly successful solutions will be human focused and will blend AI and ML techniques with the skills of expert analysts. Taking this approach, security teams can create “machine-accelerated” humans—cybersecurity professionals who work in conjunction with AI and ML to proactively identify and mitigate threats faster and more reliably, primarily through freeing up humans to focus on strategic initiatives.

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IT service security has many layers. The IT security layer; firewalls, intrusion detection and access controls. The infrastructure layer; power, network, server health and cooling. And, most important, the people layer. The right people with the right processes, tools and measures to ensure everything else is in working order. Artificial intelligence (AI) will by far have the biggest impact on the tools and measures that people use by amplifying capabilities, streamlining processes and increasing efficiencies.

AI and deep learning will become a necessity in parsing and analyzing the mountain of data generated within a data center to more effectively manage service delivery while mitigating risks like outages. This stems from the recent transformation in how we deliver application workloads.

Too much data?

In the last 10 years, we’ve moved from mostly single server single applications to distributed applications that run in containers. These are now being delivered by micro-services running on-premise and in the cloud–all managed by automation tools. Infrastructure has become part of the application, while other applications have become part of the infrastructure. If you are using a platform like Amazon S3 or Google Maps as an integral component of your service delivery, then you are experiencing this transformation first-hand.

The resulting impact on data center management is significant with power and cooling becoming just a fraction of what needs regular attention. Environmental controls, physical devices, virtual machines and public clouds all need to be monitored and managed round-the-clock to achieve efficiencies in cost and performance. Understanding where and when to move specific workloads becomes paramount.

The amount of data an enterprise collects, monitors and analyzes today to ensure business continuity has exploded. Consider the data generated just from sensors, applications, access control systems, power distribution units, UPS, generators, and solar panels. Add to that external data sources like application vulnerability information, power rates and weather forecasts. Robust data center infrastructure management (DCIM) tools are needed to store all of this data, analyze it and turn it into actionable intelligence. You can try to compartmentalize some of this, but it is becoming increasingly difficult.

AI and deep learning are becoming integral in data center and critical infrastructure management. Here are some of the more notable areas:

• Situational awareness
  Active dashboards with trends, correlations analysis and recommended actions.
• Preventive maintenance
  Deep learning used to identify and correlate data that predicts a failure in power, storage or network connection. This allow operators to mobilize and pro-actively move workloads to safer zones, while maintenance is being performed.
• Root cause analysis
  Machine learning used to trace the failure of several services to a root cause. This becomes learned and used for future preventive maintenance.
• Network security and intrusion detection
  Machine learning and deep neural networks used to spot unusual patterns in application sensors, access control systems and network systems–and provide better signal-to-noise and pro-active mitigations. Learning neural networks are used to continuously improve the enterprise’s security posture and ability to manage related issues.
• Automation
  A “Narrow AI” equipped with various automated mitigation techniques and resulting actions similar to a car applying the brakes if it sees an imminent collision.

Deep neural networks and machine learning algorithms will improve over time, allowing for higher efficiency and performance to match fast growing application workloads. With all of this on the horizon, there’s little doubt that AI will have a massive impact on how enterprises manage their data center.

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