The scale and the surge in attention to artificial intelligence is not a new concept as the ideology behind the human-machine collaboration has been floating around since the 1980s but various factors contributed to the idea being put on hold for a while especially the lack of attention and funding. Billions of dollars are being annually invested in the industry for its research and development. The evolution of hardware and software programs and the innovation of cloud processing and computing power has put an additional advantage to the future of artificial intelligence. Here are four factors that have contributed to the growth of artificial intelligence

Availability of enormous amounts of data

The innovation of cloud storage has enabled easy access to otherwise locked data that wasn’t made available to the public. Before cloud storage became mainstream, accessing data was a costly affair for data scientists in need of data for research, but now governments, research institutes, businesses are unlocking data that were once confined to tape cartridges and magnetic disks. To train machine learning models, data scientists need enough data for precise accuracy and efficiency. With the easy availability of data, research facilities now have the opportunity to train ML models to solve complex problems with data available to them.

Advancement in innovations

The innovation of a new breed of processors like the graphics processing unit(GPU) the training process of ML models is now up to speed. The GPU comes with thousands of cores to aid in ML model training. From consumer devices to virtual machines in the public cloud, GPUs are essential for the future of artificial intelligence. Another innovation that is aiding the growth of artificial intelligence is the Field Programmable Gate Array. The FPGA is programmable processors customized for a specific kind of computing work such as training ML models. Traditional CPUs are designed for general purpose computing but FPGA can be programmed in the field after they are manufactured. Furthermore, the easy availability of bare metal servers in the public cloud is attracting data scientists to run high-performance computing jobs.

Competition drives efficiency and interests

With machine learning and deep learning, AI applications can source for data and analyze new information that can be of advantage to organizations and industries alike. This breeds rivalry between organizations who want efficiency. And these competitive advantages have had an impact accelerating the growth of artificial intelligence as firms would like to have an upper advantage over one another. Financial boosts from the majority of big companies have led to a rapid interest in AI technology and development.

Artificial Intelligence also plays a key role in revolutionizing the Software Quality Assurance testing processes. With the increasing complexity of the applications, the SQA has become a bottleneck to the success of the software projects as yet most of the agile testing processes implement manual testing to test the applications.

This is where Artificial Intelligence can help accelerate the manual testing process. With the help of AI, the QA testers can work on the most malicious functions first after they prioritize the test cases based on the existing test cases and logs.

Advancement in deep learning

Deep learning is a type of artificial intelligence course that allows systems to learn patterns from data and subsequently improve their experience. Deep learning and artificial neural networks are the most essential part of artificial intelligence growth.  Artificial neural networks are developed to mimic the human brain and can be trained on thousands of cores to speed up the process of generalizing learning models. Artificial neural networks are replacing traditional machine learning models. Innovative computer technologies such as Single Shot Multibox Detector (SSD) and Generative Adversarial Networks (GAN) are revolutionizing image processing. The ongoing research in computer vision will become important in artificial intelligence healthcare and other domains. The emergence of ML techniques such as Capsule Neural Networks (CapsNet) and Transfer Learning will consequently change the way ML models are trained and deployed. They will be able to accumulate data that are precise in problem-solving and data analysis to give accurate predictions and results.

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In industrial settings, robots often are used for tasks that require repetitive grasping and manipulation of objects. Soft robotic grippers and actuators have attracted increasing attention due to safer and more adaptable human-machine and environment-machine interactions than their rigid counterparts.

Now, a team of engineers at Michigan State University has designed and developed a novel soft humanoid hand that is capable of robustly grasping a variety of objects with different weights, sizes, shapes, textures, and stiffnesses much in the same way humans do.

When designing the new model, the lead author Changyong Cao and his team observed several human-environment interactions, from fruit picking to sensitive medical care. By studying all the interactions, the team has developed a prototype that demonstrates the merits of a responsive, fast, lightweight gripper capable of handling a multitude of tasks that traditionally required different types of gripping systems.

The prototype has four fingers and an opposable thumb; each soft hand finger is made of flexible hybrid pneumatic actuators (FHPAs). These actuators use pressurized air to allow each of the fingers to move independently. Inside the fingers is a bone-like spring core, which is built around a unique structural combination of the actuated air bladder. This new FHPA achieves a balance of required flexibility and necessary stiffness; thanks to this, the gripper has the capabilities of large grasping force, ease of fabrication, and repair.

“The novel humanoid hand design is a soft-hard hybrid flexible gripper. It can generate larger grasping force than a traditional pure soft hand, and simultaneously be more stable for accurate manipulation than other counterparts used for heavier objects,” said Changyong Cao.

In addition to fruit picking and medical treatment, the invention can be used, for example, in product packaging, manipulation of fragile objects, rehabilitation, and surgical robotics.

In the future work, the team hopes to combine its advances with Cao’s recent work on so-called ‘smart’ grippers, integrating printed sensors in the gripping material. They are also looking for the possibilities of combining the hybrid gripper with ‘soft arms’ models, which would allow the machine to more accurately mimic precise human actions.

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What on earth is machine learning?

Machine Learning (ML) is a relatively new field and has been talked about quite a bit in the technology sphere. It’s one of the most important advancements since the invention of computers. While computers were these ubiquitous machines that could do a whole lot of things, they essentially sped up the process of executing tasks and could do so tirelessly, repeatedly and accurately. The only drawback if there was one, was that they needed to be “taught” how to execute these tasks through a set of instructions called programs or applications. So if one wanted to speed up banking processes, it needed an application to be developed for it, if one wanted a faster pace of human resource functions in a company, another application would need to be developed, and so on.

Another drawback was that these applications were designed with the idea that humans would make the decisions unless it was based on logic alone. This is where most systems became flawed. Humans are notoriously incapable of consistency, unable to prevent emotions from influencing their decisions and absolutely cannot keep pace with machines.

With ML, there is no more the need to programme a computer to understand a specific process. If an ML “engine” is simply exposed to a certain process, it can “learn” by itself using statistical models and predict outcomes based on its observations. Not only can it do something really quickly and relentlessly, it can even make better decisions than humans. This has deep implications on the way our society functions and gives birth to new possibilities.

Imagine an ML engine being implemented in the field of insurance. It has the potential to predict the outcome of a certain policy with great accuracy. This will reduce the premiums for every subscriber as there will be no need to set aside huge amounts in the event of errors in estimating the likelihood of a subscriber making a claim. It can even be employed to create customised policies for every person depending on their specific profiles, needs, environmental conditions or even their social media pages and fitness tracker data!

You may have already heard of self-driving cars that are safer than any human driver and ML engines reading medical images such as X-rays and CAT scans more accurately than most doctors trained in the fields — these are additional areas where ML engines are creating massive impact not only on the profitability of businesses, but also on the quality of life for people.

The ubiquity and pervasiveness of ML can further be understood from the fact that patents for this technology were the third-fastest growing category of all patents with a growth rate of 34% between 2013 and 2017. A Deloitte Global report predicts that the number of ML pilots and implementations doubled in 2018 over 2017 and will double again by 2020. International Data Corporation (IDC) forecasts spending on AI and ML to grow from $12 billion in 2017 to $57.6 billion by 2021.

What can UX do to help?

With machines doing the heavy lifting, the relationship between humans and machines is changing and from my vantage point as a UX designer, I wondered what role we would play in defining the evolving human-machine interface. In the distant future, we will undoubtedly work with artificial intelligence systems using voice and brain-wave based interfaces. But in the near future, there are interesting opportunities because the role of the user of an application changes to that of a trainer. The role of UX designers, consequently, will be to design applications in such ways as to make this training more efficient.

ML engines are based on statistics, which means that their accuracy is entirely dependent on the quantity of background data they can use for the “observations”. Some systems that have been around for a long time have large quantities of such background data. Others that have been implemented recently don’t have the amount of data that may allow the ML engines to make accurate determinations of outcomes. In such cases, we need to generate the data that the ML engines can use to learn to predict outcomes.

There are several interesting ways of doing this and it’s probably best illustrated with an example. Imagine a bank which provides several types of loans such as personal, automobile or business loans to its customers. Each loan application first needs to be verified for completeness, accuracy of information provided, eligibility of the applicant based on the loan type, size and collateral and a thorough examination of supporting documents.

The application verifier probably clicks the “Approve” button once s/he sees the details that have been provided by the customer and cross-references that against the type and size of loan at which point, it goes over to the next person in the process. If we imagine this person as a trainer instead, they could first indicate their confidence level (on a scale of 1 to 10) in the accuracy of the information they have verified — one for completeness, one for accuracy of information and one for sufficiency of collaterals provided for the loan. So instead of one “Approve” button as a data point, the system now gets three and that too on a scale of 1 to 10 which is more than a 300% increase in the quality of data provided to the ML engine. This means that the ML engine will be as capable of predicting the outcome of this step with 300% less data than a system that is designed to have only one “Approve” button.

Just like there are some people who are better teachers in real life, there will be better trainers for ML engines as well who will provide more accurate data. But the ML engine will be able to normalise this data if we provide feedback mechanisms at the end of the process and show it which approvals resulted in loans that were paid back successfully and which weren’t. So designing that kind of a feedback mechanism at the end will be important.

There are many other aspects of design that can be applied to make all this better, but the core principle is the same — that software systems of the future should imagine the user as a trainer and design all interactions based on that idea. Doing so will make ML-based systems much more effective and accurate much earlier than when this principle is not considered.

When should I engage UX designers for my business?

A prospective client approached us to design a financial marketplace based on processes that occur in the real world. We identified the complexities that would prove to be challenges for the smooth functioning of the marketplace. First, by going online, the lead generation funnel would just become wider. Since these leads would then need to be handled by agents of the company, that point would undoubtedly become a bottleneck, affecting performance. Secondly, approvals were required at every stage of the process, thus needing human intervention at each stage. This would be the second point where the speed would be reduced.

So when should you engage a UX designer? As early as possible, so you can not only save costs, but also increase your revenues?

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British science fiction author Arthur C. Clarke famously wrote in 1962 that “any sufficiently advanced technology is indistinguishable from magic.” In 2017, Artificial Intelligence (AI) seems to be that form of “advanced technology” that is seemingly putting magic into shade.

But what is this magical form of technology? Simply put, AI is based on an algorithm that takes data input in the form of text, speech, video and/or image; processes it through an artificial neural network inspired by the human brain; and generates a decision or an action as the output, which it takes as a learning for future and continues to self-improve, much like an infant. But AI as a technology had been languishing in research labs until a decade ago for want of insane amount of data and computing power it needed to be of any consequence. However, four changes have taken place to spur this revolution in recent times: recent breakthroughs in deep learning; exponential growth in brute processing power and big data; cheaper data storage; and ubiquity of internet-enabled smart mobile devices. It was only last year that Google’s DeepMind AI agent outgunned humans and mastered the infamously difficult Atari game Montezuma’s Revenge, and in 2011 IBM’s Watson famously beat two all-time Jeopardy! champions. But AI is more than becoming video game jocks.

We believe that AI is fast evolving from an obscure curiosity of the last decade to a powerful utility of the current one. There are physical as well as virtual uses, some of which have been in the news ad nauseam while many remain inconspicuous. In the Indian context, corporations can particularly extract a huge business advantage from “Physical-Inconspicuous” and “Virtual-Obvious” applications of AI due to relative ease of implementation.

While that happens, there are three fundamental shifts we all will witness. Firstly, the human-machine interface will drastically change. The web will become less relevant, and the influence of apps will diminish. Customers will instead expect to ask natural questions to get their devices to find data and either present it through a friendly interface or have their request actioned. Secondly, language barriers across the countries and communities will disappear. Dramatic improvements in Natural Language Processing (NLP) will create seamless interaction for culturally and linguistically diverse populations. Recently, Google Translate AI invented its own artificial language to translate language pairs on which it had not been explicitly trained, and this is just the warm-up. And thirdly, the ability to use artificial intelligence and machine learning to enhance decision making or to create autonomous environments will be the key to survival. According to Gartner, by 2019, artificial intelligence platform services will cannibalize revenues for 30% of market-leading companies.

With the buzz around AI, many CEOs today are already keen on harnessing it to their advantage. However, to be able to achieve that, they need to surgically identify where all AI can create the most significant and durable advantage. BCG identified a four-point action framework for executives to help them shape and harness the advantage from AI.

Identify customer needs: Identify the current and potential customers’ explicit and implicit unmet needs.

Adopt technological advances: AI has the ability to improve outcomes and lower cost, so welcome the change in your industry. Invest in holistic AI infrastructure. Explore AIaaS (Artificial Intelligence as a Service) model.

Build and use data sources: Identify, build, and combine existing data with new and even external sources such as databases, optical signals, text, graphs, and speech.

Decompose processes: Break down processes into relatively routinized and isolated elements that can be automated using AI. Then, reassemble them to better meet your customers’ needs.

While there are tremendous advantages, there is a multitude of grey areas that remain to be addressed. For one, AI needs to be regulated at a global level to channelize it in the right direction. Newly founded Partnership on AI is a step in the right direction but legislative measures must also be in place. Also, as the AI systems continue to self-learn and achieve/exceed capabilities of a human brain, we must have humans in the loop in some form in all applications of AI. A framework is needed to give companies, customers and users confidence in the outcomes of self-learning AI systems. For example, why should a doctor/patient trust the AI-based diagnosis and prescription? Finally, the fear of job losses can hinder the pace of AI adoption. Our thinking is that only the easy-to-automate administrative and repetitive tasks in a job will be eliminated, and job profiles will change to focus on things only humans can do. We see jobs getting enriched further, with increased application of human creativity, collaboration, empathy, and judgment. Demand for data scientists will increase, but contrary to general perception, that will be the case only in the short-term. In our view, the demand for their skill is likely to diminish over time as AI systems cross the threshold of completely autonomous self-learning.

These open questions are expected to be addressed as the spectrum of AI disruptions expands. Corporations, however, must start embracing AI early, as it is going to disrupt their industries sooner than one can anticipate.

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