Companies are seeing the benefits of machine learning and artificial intelligence, but many are unsure about how to truly leverage these innovations in the tech space, writes WERNER VOGELS, CTO of Amazon.com.
When a technology has its breakthrough, can often only be determined in hindsight. In the case of artificial intelligence (AI) and machine learning (ML), this is different. ML is that part of AI that describes rules and recognizes patterns from large amounts of data in order to predict future data. Both concepts are virtually omnipresent and at the top of most buzzword rankings.
Personally, I think – and this is clearly linked to the rise of AI and ML – that there has never been a better time than today to develop smart applications and use them. Why? Because three things are coming together. First: Users across the globe are capturing data digitally, whether this is in the physical world through sensors or GPS, or online through click stream data. As a result, there is a critical mass of data available. Secondly, there is enough affordable computing capacity in the cloud for companies and organizations, no matter what their size, to use intelligent applications. And thirdly, an “algorithmic revolution” has taken place, meaning it is now possible to train trillions of algorithms simultaneously, making the whole machine learning process much faster. This has allowed for more research, which has resulted in reaching the “critical mass” in knowledge that is needed to kick off an exponential growth in the development of new algorithms and architectures.
We may have come a relatively long way with AI, but the progress came quietly. After all, during the last 50 years, AI and ML were fields that had only been accessible to an exclusive circle of researchers and scientists. That is now changing, as packages of AI and ML services, frameworks and tools are today available to all sorts of companies and organizations, including those that don’t have dedicated research groups in this field. The management consultants at McKinsey expect that the global market for AI-based services, software and hardware will grow annually by 15-25% and reach a volume of around USD 130 billion in 2025. A number of start-ups are using AI algorithms for all things imaginable – searching for tumors in medical images, helping people learn foreign languages, or automating claims handling at insurance companies. At the same time, entirely new categories of applications are being created whereby a natural conversation between man and machine is taking center-stage.
Progress through machine learning
Is the hype surrounding AI and ML even justified? Definitely, because they offer business and society fascinating possibilities. With the help of digitization and high-performance computers, we are able to replicate human intelligence in some areas, such as computer vision, and even surpass the intelligence of humans. We are creating very diverse algorithms for a wide range of application areas and turning these individual pieces into services so that ML is available for everyone. Packaged into applications and business models, ML can make our life more pleasant or safer. Take autonomous driving: 90% of car accidents in the US can be traced to “human failure”. The assumption is that the number of accidents will decline over the long term if vehicles drive autonomously. In aviation, this has already been reality for a long time.
MIT pioneers Erik Brynjolfsson and Andrew McAfee predict that the macroeconomic effect of the so-called “second machine age” will be comparable to what the steam engine once unleashed when it replaced humans’ muscular strength (“the first machine age”). Many are uncomfortable with the idea that an artificial intelligence exists alongside human intelligence. That is understandable. We must therefore discuss – parallel to the technological developments – how humans and AI can co-exist in the future; the moral and ethical aspects that arise; how to ensure we have a good grip on AI; and which legal parameters we need in order to manage all this. Answering these questions will be just as important as the effort to solve the technological challenges, and neither dogmas nor ideologies will help. Instead, what’s needed is an objective, broad-based debate that takes into account the wellbeing of society as a whole.
Machine Leaning at Amazon
For the past 20 years, thousands of software engineers at Amazon have been working on ML. We dare to claim that we are the company that has been applying AI and ML as a business technology the longest. We know that innovative technologies always take off whenever barriers to entry fall for market participants.
That is the case right now with AI and ML. In the past, anyone who wanted to use AI for himself had to start from scratch: develop algorithms and feed them with enormous amounts of data – even if he later needed an application for a strictly confined context. This is referred to as so-called “weak” AI. Many of the consumer interfaces that everyone is familiar with today, such as recommendations, similarities or autofill functions for search prediction – they are all ML driven. In the meantime, they can predict inventory levels or vendor lead times, detect customer problems and automatically deduct how to solve them; and discover counterfeit goods and sort out abusive reviews, thereby protecting our customers from fraud. But that is only the tip of the iceberg. At Amazon, we are sitting on billions of historical order information data, which allows us to create other AI/ML-based models based on AI for many different kinds of functionalities. For example programming interfaces that developers can use to analyze images, change text into true-to-life language or create chatbots. But ultimately, there is something to be found for everyone who wants to define models, train them, and then scale. Pre-configured, attuned libraries and deep learning frameworks are widely available, which allow anyone to get started very fast.
Companies like Netflix, Nvidia, or Pinterest use our capabilities in ML and deep learning. More and more layers are being created in a kind of ecosystem on which companies and organizations can ‘dock’ their business – depending on how deep they want to, and are able to, immerse themselves in the subject matter. Decisive is the openness of the layers and the reliable availability of the infrastructure. In the past, AI technologies were so expensive that it was hardly worth it to use them. Today, AI and ML technologies are available off the shelf, and they can be called up according to one’s individual requirements. They form the basis for new business models. Even users who are not AI specialists can very easily and affordably incorporate the building blocks into their own services. In particular small and medium-sized companies with innovative strength can benefit. They do not have to learn any complex ML algorithms and technologies, and they can experiment without incurring high costs.
Artificial intelligence helps to satisfy the customer
One of the most advanced areas of application is e-commerce. AI-supported pre-selection mechanisms help companies to free their customers’ decision making from complexity. The ultimate goal is customer satisfaction. If there are only three types of toothpaste, the customer can easily pick one and feel good about it. When more than 50 kinds are on offer, the choice becomes complicated. You have to decide, but you’re not sure if the decision is the right one. The more possibilities there are, the more difficult it becomes for the customer. Our best-known algorithms come from this field: filtering product suggestions based on one’s purchase history of products with similar attributes, or on the behavior of other customers who were interested in similar things.
Of course, consistent quality also contributes to the satisfaction of the customer. Intelligent support makes life easier for the provider and the customer. For Amazon Fresh, for example, we have developed algorithms that learn how fresh groceries have to look, how long this state lasts, and when food should no longer be sold. Airlines or rail transport companies could also use this for their quality control by running an algorithm based on the image data of the freight; the algorithm would recognize damaged goods and automatically sort them out.
If you can predict demand, you can plan more efficiently
In B2B and B2C businesses, it is critical that goods are available quickly. It is for this reason that we at Amazon have developed algorithms that can predict the daily demand of goods. This is particularly complex for fashion goods, which are always available in many different sizes and variations and for which reorder possibilities are very limited. Information about past demand, among others, is fed into our system, as well as fluctuations that can occur with seasonal goods, the effect of special offers, and the sensitivity of customers to price shifts. Today we can predict precisely how many shirts in a certain size and color will be sold on a defined day. We have tackled this issue and made the technology available to other companies as a web service. MyTaxi, for example, benefits from our ML-based service to plan at what time and at which place the customer will need the vehicle.
New division of labor
But AI is much more than just forecasting. In the field of fulfillment, which is relevant for numerous industry sectors, we are thinking of ideas of how AI can contribute the most to taking another step away from a Tayloristic work pattern. Applied in robots, AI can free people from routine activities that are physically difficult and often stressful. Machines are very good at, and sometimes even outperform, tasks that are complicated for a human to do, such as finding the optimal route in a warehouse for a certain number of orders and transporting heavy goods to the point where it is sent to the customer. For supposedly easy tasks, by contrast, the robot is overwhelmed; an example is recognizing a box that has landed on the wrong shelf. So how to bring together the best of both players? By letting intelligent robots learn from humans how to identify the right goods, take on various orders and navigate their way autonomously through the warehouse on the most efficient route. This is how we take away the most tedious part of the task and shift resources to more interaction with the customer.
Our client SCDM uses the core idea of freeing up resources for “human” strengths, but in a completely different context. SCDM is a service provider that supports banks and insurance companies with digitization. Using AI, SCDM enables its customers to classify documents that are of very different formats (PDF, Excel or photos), for example a report about the performance of an investment product that contains hundreds of pages. By scanning hundreds of thousands of documents simultaneously, SCDM’s algorithm recognizes which document is relevant for a specific request, finds out where relevant data for a specific type of preparation is located, and then extracts the data from the document. As a result, there is less bias and fewer errors in the number crunching, and more time for human interaction with important stakeholders like investors, analysts and other customers.
Machine learning in education, medicine and development aid
In addition to their potential for things like efficiency and productivity, ML and AI can also be used in education. Duolingo, which offers free language course apps, uses text-to-language algorithms to assess and correct learners’ pronunciation. In medicine, AI supports doctors in analyzing X-Ray CTs or MRT images. The World Bank also uses AI in order to implement infrastructure programs, development aid and other measures in a more targeted manner in the future.
More room for optimism
Despite all these developments, many people from academia, business and government have a critical view of ML and AI. There have been warnings that a new super-intelligence is jeopardizing our civilization – and these warnings have been effective in attracting publicity.
However, neither hysteria nor euphoria should be allowed to get the upper hand in the public debate. What we need instead is a pragmatic-optimistic view of the emerging possibilities. AI enables us to get rid of tasks in our work which damage our health or where machines are better than we are. Not with the goal of making ourselves redundant. Rather, in order to gain more personal and economic freedom – for interpersonal relationships, for our creativity and for everything that we humans can do better than machines. That is what we should strive for. If we don’t, we will ultimately forego the economic and societal opportunities that we could have grasped.
Epic Games brings a
Nite-mare to Android
Epic Games’ decision to not publish games through Google Play inadvertently opens a market to Android virus makers, writes BRYAN TURNER.
Epic Games, the creator of Fortnite, decided to take the high road by skipping Google Play’s app distribution market and placing a third-party installer for its games on its website. While this is technically fine, it is not recommended for the average user, because allowing third-party installers on one’s smartphone opens up the possibility of non-signed and malicious software to be run on the smartphone.
In June, malware researchers at ESET warned Android gamers that malicious fake versions of the Fortnite app had been created to steal personal information or damage smartphones. A malware researcher demonstrated how the fake applications works in the Tweet below.
Example how you can get infected by downloading #Fortnite Android app from YouTube video with 130K+ views.
This one send SMS to premium rate number and downloads another fake app. pic.twitter.com/pYj8GZoqoZ
— Lukas Stefanko (@LukasStefanko) June 21, 2018
While the decision to bypass Google Play was a bold move on Epic Games’ part, it has been a long time coming for app developers to move their premium apps off Google’s Play Store. The two major app distributors, Google Play and Apple’s App Store, take a 30% cut of every purchase made through their app distribution platforms.
The App Store is currently the only way to get apps on a non-modified iOS device, which is why Epic Games had no choice for Fortnite to be in the App Store. On the other hand, Android phones can install packages downloaded through the browser, which makes the Play Store almost unnecessary for the gaming company.
The most interesting part of this development is that Google is not the “bad guy” and Epic Games is no saviour to other game developers. Epic Games is a company with a multi-billion dollar valuation and has resources like large-scale servers to distribute and update its games, a big marketing budget to ensure everyone knows how to get its games, and server security to protect against malware.
Resources of this scale allow the game company to turn a cold shoulder to Google’s Play Store distribution and focus on its own, in-house solution.
That said, installing packages without the Google Play Store must be done carefully, and it is essential to do homework on where a package is downloaded. Moreover, when a package is installed outside of the Google Play Store, a security switch to block the installation of third party apps must be turned off. This switch should be turned back on immediately after the third party package is installed.
This complex amount of steps makes it less worthwhile to install third party apps, in favour of rather waiting for them to reach the Play Store.
From a consumer perspective, ESET recommends not installing packages outside of the Google Play Store and to ignore advertisements to download the game from other sources.
How to take on IoT
The Internet of Things (IoT) is coming, whether you like it or not and organisations today will look to platforms and services that help them manage and analyse the streams of data coming from connected devices, says RONALD RAVEL, Director B2B South Africa, Toshiba South Africa.
Today, we are witnessing an explosion in IoT deployments and solutions and are moving towards a world where almost everything you can imagine will be connected. While this opens the door to many possibilities it also comes with its own challenges such as privacy and security.
The Internet has become an integral part of everyday life; it has been a free for all on a daily basis. IoT is a difficult concept for many people to wrap their minds around. Essentially, nearly every business will be affected.
Managing vast quantities of data across increasingly mobile workforces can be tremendously beneficial if done well, but equally can be cumbersome and ineffective if not managed properly. This is why technologies such as mobile edge computing are becoming increasingly popular, helping to increase the prevalence of secure mobile working and data management in the age of IoT.
The evolution of IoT, despite rapid and ongoing technological innovation, is still very much in its fledgling stages. Its potential, though, is demonstrated by the fact that by 2020, Bain anticipates a significant shift in uptake, with roughly 80 per cent of adoptions at that point to have progressed to the stage of either ‘proof of concept’ or extensive implementation. This means that technological innovation in IoT for the enterprise is progressing at a similarly fast rate with many of these solutions being developed with utilities, engineering, manufacturing and logistics companies in mind.
Processing at the edge
For IoT to be adopted at the rate predicted, technology which does not overwhelm current or even legacy systems must be implemented. Mobile edge computing solves this. Such solutions offer processing power at the edge of the network, helping firms with a high proportion of mobile workers to reduce operational strain and latency by processing the most critical data at the edge and close to its originating source. Relevant data can then be sent to the cloud for observation and analysis, thereby reducing the waves of ‘data garbage’ which has to be processed by cloud services.
A logistics manager can feasibly monitor and analyse the efficiency of warehouse operations, for example, with important data calculations carried out in real-time, on location, and key data findings then sent to the cloud for centrally-located data scientists to analyse.
The work of wearables
The potential of IoT means it not only has the scope to change the way people work, but also where they work. While widespread mobile working is a relatively new trend in industries such as banking and professional services, for CIOs in sectors where working on the move is inherent – such as logistics and field maintenance – mobility is high on the agenda.
Wearables – and specifically smart glasses – have started to gain traction within the business world. With mobile edge computing solutions acting as the gateway, smart glasses such as Toshiba’s assisted reality AR 100 viewer solution have been designed to benefit frontline and field-based workers in industries such as utilities, manufacturing and logistics. In the renewable energy sector, for example, a wind turbine engineer conducting repairs may use assisted reality smart glasses to call up the schematics of the turbine to enable a hands-free view of service procedures. This means that when a fault becomes a barrier to repair, the engineer is able to use collaboration software to call for assistance from a remote expert and have additional information sent through, thereby saving time and money by eradicating the need for extra personnel to be sent to the site.
The time is ripe for organisations to look to exploit the age of IoT to improve the productivity and safety of their workers, as well as the end service delivered to customers. In fact, Toshiba’s recent ‘Maximising Mobility’ report found that 49 per cent of organisations believe their sector can benefit from the hands-free functionality of smart glasses, while 47 per cent expect them to deliver improved mobile working and 41 per cent foresee better collaboration and information sharing. Embracing IoT technologies such as mobile edge computing and wearable solutions will be an essential step for many organisations within these verticals as they look to stay on top of 21st century working challenges.