Outsourcing is the silver bullet of the IT age. Everything can be made more cheaply and more profitably by sourcing the work from places with a lower cost of living. But do those places always have to be overseas? Not necessarily.

The perils of overseas outsourcing are well known, ranging from communication through cultural or time zone incompatibilities. What if there were a way to eliminate those incompatibilities while still retaining the benefits of workers who live in an area with a lower cost of living? This is possible through “insourcing.”

There are regions of the United States where the cost of living is quite low, allowing companies to have their cake and eat it too. Work can be done much more cheaply and there is no communication barrier. Additionally, impoverished areas benefit from employment.

Sounds good, you say, but what is the catch? Why hasn’t it been done already?

The main reason why companies outsource instead of insource is brain drain. Often when workers increase their technical skill, they also leave low-wage areas for high-wage areas. So, even though work can be done for a lower wage, people still living in the area have not had the necessary training for a high-tech IT job. On the other hand, when a company is outsourcing to a developing country, it is possible to have both low wages and high technical skill.

Yet coding is not the only sort of technical work needed. Thanks to artificial intelligence, there is now technical work that can be done by most people.

Yes, it is a paradox. Artificial intelligence was supposed to wipe out all our jobs but instead it has opened new avenues for employment. If we were to take a step back from the present and look at history as a whole, this would not be so surprising. Most technological innovations, while eliminating jobs, have tended to create whole new planes of productivity.

Just look at the internet as an example. It greatly undermined the power of established media but it has opened up whole new venues of expression — and not just cat videos. Think of online historical documentaries like The Biology of the Second Reich or intelligent social commentary like Alternative Math These types of media were not very practical to produce and distribute before the internet. The only reason people think that this time things will be different is that they think artificial intelligence can replace human intelligence.

It turns out that artificial intelligence is not very intelligent. The models must be trained with enormous amounts of data to learn even simple tasks. And where does all this data come from? It cannot come from artificial intelligence. It has to come from people. And that is where insourcing comes into play.

The great thing about human intelligence is that it is possessed by all humans. The level of training necessary to have human intelligence is zero. Gone is the technical training barrier. Thanks to artificial intelligence absolutely everyone can have a role to play in the new technological transformation. This is due to a very simple calculus. Since artificial intelligence needs so much human intelligence, and at the same time is so useful, then we are bound to see the inverse of Ray Kurzweil’s Singularity : artificial intelligence will cause an explosion in the need for human intelligence within IT systems.

Artificial intelligence will become widely adopted, which will drive a need for human intelligence to train the models, which will cause even more success for artificial intelligence. It will require even more people employed training the models. It is a virtuous cycle, making humans essential for all IT systems of the future. And if it is an exponential explosion, then demand will outpace the number of humans which will bring about a demographics spring.

So, enough high flying theorizing. Back to the idea of insourcing. Since we’ve established that the new IT environment will absolutely depend on human intelligence, the next big frontier will be insourcing. The advantage of developing nations that their technical skilled workers are cheaper will matter much less. We are back to only requiring lower wages to create a competitive advantage. In which case, insourcing makes a great deal of financial sense because barriers introduced by sending work to foreign countries are eliminated.

Indeed, I predict that, thanks to artificial intelligence, ordinary human intelligence will become the new oil.

The post WILL ORDINARY HUMAN INTELLIGENCE BECOME THE NEW “OIL”? appeared first on Artificial Intelligence.

AI has transformed the nature of the eCommerce business in many ways. Artificial Intelligence or machine intelligence replaces human intelligence with machines that possess the same cognitive functions of learning and problem-solving as humans.

The best part about AI is that it can perform cognitive operations with more accuracy and a faster speed than humans. It is the foremost reason for which eCommerce businesses use AI. From improving customer experience to business market research automation, AI brings about a revolution in the eCommerce industry. Consumer insights is another area in which AI is doing wonders. In this article, we will be discussing four ways in which AI is transforming consumer insights.

Why Is Market Research for eCommerce Essential?

Market research has become essential for eCommerce. From ensuring a better consumer experience to gaining quality consumer insights, market research holds great significance in different spheres of eCommerce businesses. It is impossible to gain relevant and genuine consumer insights without proper market research. Moreover, without accurate consumer insights, you cannot improve customer satisfaction as consumer insights play an essential role in understanding your customers.

Market research helps companies gain competitive and actionable consumer insights that can be used for text analytics and sentimental analysis. It is also the best way to know your target audience and the latest trends in the market. Businesses can generate more leads if they know their consumers better. Moreover, you can also know about your competitor’s strategies with market research. It helps you to learn how you can improve your business. Besides, market research also helps in promotion and advertising.

Earlier traditional market research methods such as surveys and polls were the easily accessible options. However, they are time-consuming and expensive. In the age of digital transformation, AI-driven market research has replaced traditional market research to a great extent. It has resulted in tremendous growth of AI text analytics tools and eCommerce marketing platforms.

Below Are Four Ways AI is Transforming Consumer Insights

Market Research: AI-driven market research for eCommerce helps you get actionable and competitive consumer insights faster. There are eCommerce platforms such as Revuze that use AI tools to gain real-time consumer insights that can be used for sentimental analysis. Earlier surveys and polls were the prominent methods of performing market research. However, they are time-consuming and expensive procedures. Moreover, they are not very reliable. However, market research conducted using AI tools is faster and more accurate. AI tools can be used to extract large amounts of relevant data and user-generated content.

Data Cleaning: Data analytics cannot be performed on inadequate or irrelevant data. Insufficient data comprises missing, irrelevant, duplicate, and inconsistent or data with errors. Performing data analytics on bad data leads to inaccurate results and consumer insights. Therefore, it is crucial to clean the extracted data before running data analytics. However, this process is too time-consuming when done manually. Moreover, it can further lead to human errors. Nonetheless, AI is changing the way companies perform data cleaning. AI algorithms substitute insufficient data with useful data to make it fit for analysis.

Understanding Open-ended Questions: Analysing open-ended questions is not an easy task. Open-ended questions can generate quality insights. However, the main challenge is to analyze open-ended feedback and questions. With a human-centric approach, there are chances of errors and biased analysis. Moreover, it’s time taking and tedious. However, interpreting open-ended questions with AI algorithms is free from any biases and errors. Also, it is less time consuming and less costly.

Faster Insights: With AI, gaining quick consumer insights has been made possible. Surveys and polls are time taking market research methods to gather consumer insights. With the traditional market research methods, taking appropriate actions on the insights takes a long time, and it is not suitable for your business. Whereas, with AI, you can collect actionable and competitive insights in no time.

Faster insights mean that you can act and respond to your customer’s needs in less time. As a result, more customers will be attracted to buy from you. It will, in turn, result in increased sales and profit.

The post Four ways AI is changing consumer insights appeared first on Artificial Intelligence.

Within a short span of time and with few instructions, people can solve complex problems from scratch, for instance, loading the trunk of a car with seemingly too many objects. This is the core of human intelligence—its rapid and flexible nature. What is the cognitive scheme that allows us to create novel and complex strategies? And do “intelligent” machines use similar or fundamentally different schemes?

To answer these questions, scientists at the Champalimaud Centre for the Unknown in Portugal, and the University of California, Berkeley, created Hexxed. This mobile game consists of a series of fun and challenging puzzles designed to provide unique insight into how intelligence works. This free app has compatible versions for both iPhone and Android.

Taking science out of the lab

“Hexxed joins a global trend of citizen science games in which individuals around the world can contribute to scientific discoveries by simply playing,” says Gautam Agarwal, one of the scientists who developed the game as part of his research project in the lab of Zachary Mainen at Champalimaud.

Why move the experiment outside of the lab? According to Agarwal, this is the best way to gather data sets that are diverse and large enough to tap into difficult questions, such as how age and cultural background shape human thinking. “Experiments in laboratory conditions have a limited number of subjects, and are often repetitive and dull. In contrast, online games can be played by an unrestricted number of people worldwide, and inspire players to participate fully by immersing them in an evolving stream of experiences.”

Human vs. machine intelligence

The diversity of the data goes even further, as the team plans to use the game to learn not only about human intelligence, but about machine intelligence, as well. Mattia Bergomi, a researcher involved in the study, points out that video games are commonly used to test the capacity of artificial intelligence, but often fall short of the mark.

“The majority of games fall into one of two categories,” he explains. “On one extreme, there are challenging games that can only be solved with complex strategies. This results in problem-solving approaches that are difficult to formulate mathematically and therefore difficult to compare across subjects. On the other extreme, you have simple games that can easily be described mathematically. But then, those are not challenging enough to draw out intelligent problem-solving schemes.”

Hexxed was developed to bridge between the two extremes: it’s quite challenging, but it can still be described by simple mathematical constructs. “This unique design will allow us to compare strategies adopted by humans with those generated by machines in a systematic and comprehensive manner,” Agarwal adds.

Game on!

So what is intelligence and how does it vary across humans and machines? Play Hexxed and we may have an answer soon enough.

The post What makes human intelligence exceptional? The answer may be hidden inside this game appeared first on Artificial Intelligence.

Experts from the University of Leicester have released Neuroscience  that breaks with the past fifty years of neuroscientific opinion, arguing that the way we store memories is key to making human intelligence superior to that of animals.

It has previously been thought and copiously published that it is ‘pattern separation’ in the hippocampus, an area of the brain critical for memory, that enables memories to be stored by separate groups of neurons, so that memories don’t get mixed up.

Now, after fifteen years of research, Leicester University’s Director of Systems Neuroscience believes that in fact the opposite to pattern separation is present in the human hippocampus. He argues that, contrary to what has been described in animals, the same group of neurons store all memories. The consequences of this are far reaching, as such neuronal representation, devoid of specific contextual details, explains the abstract thinking that characterizes human intelligence.

Leicester University’s Director of Systems Neuroscience Professor Rodrigo Quian Quiroga explains,

“In contrast to what everybody expects, when recording the activity of individual neurons we have found that there is an alternative model to pattern separation storing our memories.

“Pattern separation is a basic principle of neuronal coding that precludes memory interference in the hippocampus. Its existence is supported by numerous theoretical, computational and experimental findings in different animal species but these findings have never been directly replicated in humans. Previous human studies have been mostly obtained using Functional Magnetic Resource Imagining (fMRI), which doesn’t allow recording the activity of individual neurons. Shockingly, when we directly recorded the activity of individual neurons, we found something completely different to what has been described in other animals. This could well be a cornerstone of human’s intelligence.”

The study, ‘No pattern sepaeration in the human hippocampus’, argues that the lack of pattern separation in memory coding is a key difference compared to other species, which has profound implications that could explain cognitive abilities uniquely developed in humans, such as our power of generalization and of creative thought.

Professor Quian Quiroga believes we should go beyond behavioural comparisons between humans and animals and seek for more mechanistic insights, asking what in our brain gives rise to human’s unique and vast repertoire of cognitive functions. In particular, he argues that brain size or number of neurons cannot solely explain the difference, since there is, for example, a comparable number and type of neurons in the chimp and the human brain, and both species have more or less the same anatomical structures. Therefore, our neurons, or at least some of them, must be doing something completely different, and one such difference is given by how they store our memories.

The post Memory Storage Study Asks How Human Intelligence Is Different appeared first on Artificial Intelligence.

Due to the public outcry and tax cuts, companies are going green with AI to prevent irreversible harm to the planet.

We are the first generation to witness the effects of climate change unfold, and the last with the ability to prevent those effects from intensifying and irreversibly harming our planet.

According to the US Environmental Protection Agency, global carbon emissions from fossil fuels rose significantly since 1900. CO2 emissions have increased by about 90% since 1970, with emissions from fossil fuel combustion and industrial processes contributing about 78% of the total greenhouse gas emissions increase from 1970 to 2011.

These insights are not new to operators of industrial plants and factories. China is followed by the US and the EU as the largest emitters of CO2. They collectively account for over 50% of global emissions.

Most industrial facilities are well aware of their carbon footprint. Due to public demand for environmental sustainability and higher taxes on emissions, they have every intention to implement decarbonization measures. For the most part, however, their efforts have fallen short, leaving a gap between their ambitions and the reality of their carbon reduction activities.

According to the Boston Consulting Group, the standard three-step approach to implementing decarbonization measures starts with assessing the carbon footprint and comparing it against the carbon footprint of industry peers, as well as national/international standards. The next step is to develop realistic short- and long-term decarbonization targets based on a thorough cost-benefit analysis. The final step is to define the specific measures that will provide environmental and economic benefits.

Most industrial plants and factories already have the necessary capabilities to go green, both in terms of production and logistics. Depending on the industry, the necessary change may be a switch in energy sources, such as solar and wind in place of coal and fossil fuels. In most cases, however, the change calls for an upgrade in processes and technologies for increased efficiency. Some companies, such as the Israel-based EcoPlant, are working to tackle that challenge before it’s too late.

EcoPlant, which has offices in Israel and the US, developed a SaaS (Software as a Service) solution that connects to air compressors’ controllers and property sensors to monitor and continuously optimize entire systems. Their predictive AI algorithms conduct ongoing energy surveys and dynamically control each compressor and the whole system.

The end result? Significantly reduced energy waste, increased reliability, and optimized maintenance. Such a solution enables continuous savings of hundreds of thousands of dollars annually. EcoPlant’s proprietary algorithms can also be applied in settings such as hospitals and commercial buildings, as well as critical infrastructure equipment, such as pumps and chillers, essentially making it a one-size-fits-all solution.

The harmful effects that stem from energy waste and CO2 emissions undoubtedly trace back to human decisions and errors. Undoing those negative effects, especially in the modern post-pandemic world, requires the collaborative efforts of human intelligence with machine intelligence. Numerous facilities hailing from industries with the largest carbon footprints, such as technology, automotive and process industries, are already getting on board with AI solutions such as EcoPlant, to meet popular and regulatory demands.
According to the Paris Climate Agreement, which is an ambitious global action plan to fight climate change and its negative impact, emissions from production and logistics would need to drop approximately 45% by 2030 to be on a path to meet the 1.5°C target for limiting the global temperature increase. To meet this goal, it is imperative for industrial plants and factories to ramp up their efforts to go green by turning to AI.

The post Artificial Intelligence can reliably help industrial plants go green appeared first on Artificial Intelligence.

“Artificial intelligence (AI) has a substitutive role – it ensures that difficult, dirty and dangerous work is done more and more by machines and less and less by human beings,” says Professor Frank Pasquale from Brooklyn Law School.

While AI is poised to disrupt our work and lives, these technologies can be harnessed through wise regulation. So rather than replacing individuals, AI should assist them in completing tasks that are more fulfilling or by augmenting work that is often classified as professional.

Can robots replace human intelligence?

Should people be taking more courses like computer science or technical fields that will help them understand AI better?

“Yes, but I don’t think they should replace existing courses. I think that we need to have a more expanded view of education,” says Prof. Pasquale, who will discuss how AI can promote inclusive prosperity at the annual lecture for The Allens Hub for Technology, Law and Innovation at UNSW Sydney on Tuesday 20 October at 11am.

AI can play an important role in education and helping teachers plan and deliver courses – but it is not a replacement.

Similarly in the tax arena, shifting the focus to tax robots is not the right solution. “When it comes to understanding the nuances in tax policies, tax robots are not ideal,” he said.

“We need to have a more well-rounded tax policy that is looking at excessive capital income being taxed, rather than the machines being used to generate that.”

It is also important to question universal basic income, Prof. Pasquale adds: “I think universal basic services is critical because it’s important for the future of labour policy.”

Does AI still need human intervention?

What’s really surprising over the past few years, according to Prof. Pasqual, is how tasks that many assumed would be completed by AI (self-driving cars, for example), are nowhere ready to be adopted en masse.

The self-driving car is itself a warning about issues with AI, he warns. “First, there are the basic technical problems that occur,” he said.

“Secondly, will we have to redesign roads and traffic patterns to accommodate self-driving cars? Do we rely less on public transport, or develop other forms of public transit?

“All of those are not just technical questions, but they’re very deeply political and social questions that involve a lot more than just AI.”

How can AI transform the field of law?

In many simple transactions, AI has the potential to perform work currently undertaken by certain legal personnel, such as the granting of a licence to hunt or fish.

“But there are also areas where AI can be beneficial by assisting current attorneys in deciding which case to bring in, deciding the types of arguments and doing better legal research,” Prof. Pasquale says.

“In various places that are purely financial, I could see another displacement effect where, like in tax law, it might be that we code taxes as opposed to simply having a verbal description of them.”

And Australia’s Robodebt issue, for example, highlighted the fact that excessive automation cannot be achieved successfully without human oversight.

“We have to always keep an eye on it. We’ll see a lot of enthusiasm about automating AI in law, but it’s going to be a slow process,” Prof. Pasquale says.

AI in business

AI has been increasingly playing an important role in business. Some companies, for example, have started using AI in the recruiting process by analysing candidates’ facial expressions, tone of voice and the rate of speech in job interviews.

Such AI is said to be able to determine if an interviewee would be a good employee, based on current employees’ expressions, facial patterns and tone of voice.

“I think that’s troubling because people deserve to be judged on the basis of known characteristics and known qualities, and not on an AI that’s cross-correlating thousands or millions of variables with existing employees.”

Prof. Pasquale refers to it as an ‘alien intelligence’ as it is devoid of human analysis.

However, AI can be of benefit in the medical sector, specifically when it comes to assisting doctors with improving medical diagnoses.

Looking at the Australian healthcare system, Prof. Pasquale says it would make sense to invest in AI capacity, particularly using data that Australia already has.

“Part of the future of the industrial policy of AI is convincing governments to invest in homegrown talent, and to avoid just doing the easy thing and partnering with Google or Facebook or any other big organisation,” he says.

“It is really interesting to see how the use of AI can be different in a county’s industrial strategy”

While AI has the potential to contribute to job losses if poorly planned and timed, medical AI has the power to make labour more valuable and easier.

“I think that if we can envision a future where AI is permitted to increase productivity, and increase the value of labour, then we are on a path to a much happier outcome for everybody,” Prof. Pasquale says.

Prof. Pasquale will further discuss how AI can promote inclusive prosperity at the annual lecture for The Allens Hub for Technology, Law and Innovation at UNSW Sydney on Tuesday 20 October at 11am. You can register for the online event here.

The post How can artificial intelligence promote inclusive prosperity for all? appeared first on Artificial Intelligence.

Telecommunications might not be the first sector that comes to mind when we think of industries that are considered the pioneers in customer experience. During COVID-19, the expectations for customer experience are even more profound and the stress on telecom providers couldn’t be higher. While we would not have been able to maintain our ability to work, stay socially connected, learn, shop, get healthcare, and more during the pandemic without the connectivity that telecom companies deliver, the current customer experience has tremendous potential for transformation.

Despite the growing global dependency on reliable and fast connectivity, downward trends that have impacted telecom companies for years continue to affect their present performance and hinder their ability to emerge stronger after the crisis. Slow growth due to declining ARPU (average revenue per user), industry saturation, customer churn, competitive disruptions, and the need to achieve return on investment into 5G and other technologies all continue to impact the industry.

According to Bain & Company, COVID-19 has caused a rapid and sharp reduction in new customer sign-ups. Telecoms need to transform customer experience to reverse the long-term trends of poor customer satisfaction, low loyalty scores, and high churn rates.

Telecom players are laggards in adoption of emerging technologies
Many telecommunication service providers are laggards compared to other industries when it comes to customer experience. High call volumes generated during the pandemic only served to emphasize the state of the archaic systems and processes in their contact centers. Long wait times, slow resolutions, and the challenges of remote work combined has frustrated both customers and agents.

Nearly 80 percent of people polled in a survey conducted by a leading global conversational AI company in April 2020 want to speak with an agent. Yet over 33 percent of callers waited for more than 30 minutes on hold, with 5 percent saying they waited for more than 2 hours in the same survey.

We all know customer tolerance has its limits; 72 percent of the survey respondents say they ended a call while still waiting to speak with a live person, and 43% of respondents who did get through to an agent said they felt angry, annoyed, or frustrated because they were put on hold.

AI and automation can remove friction
Artificial intelligence (AI) and automation can provide telecom companies with a way to move from a laggard position to a place among leaders in customer experience. Companies must use these technologies to deliver a frictionless customer experience anywhere along with an end-to-end conversation—from self-service channels (mobile, web, and IVR) to live agent interactions.

Boston Consulting Group(BCG) believes that AI technologies will “accelerate the rethinking of customer interaction to improve customer experience.” Citing examples of AI usage within telecom companies, BCG reports that “At a challenging time for customer service staff and field forces, AI has helped decrease the time employees spend on simple tasks and refocused them on more pressing issues.”

While some telecoms have deployed point AI or automation solutions, they have not been successful in creating the much-needed frictionless, end-to-end experience for customers. For example, many self-service channels still do not understand customer sentiments or intent, nor transfer this context as conversations move between channels. Agents have limited in-call coaching guidance or lack immediate access to customer information, such as customers’ current equipment, which increases average handle times and creates delays in order fulfillment. That is because point solutions do not cover an entire conversation and instead create additional silos within the customer journey.
A Conversational Service Automation (CSA) platform in comparison uses conversational AI, robotic process automation (RPA), and workflow automation to bridge humans and machines and back office and front office operations throughout a customer conversation and across digital and voice channels.

Customer-centric telecom experiences
Telecoms can deploy CSA to meet the new needs of customers with empathy and understanding. It can help agents be more effective and productive irrespective if they are working from home or in the contact center and still deliver a high-quality experience at lower costs. Telecoms can remove the friction that frustrates their customers by using CSA to automate and transform their customers journeys. Here’s how:
Conversational Self-Service: Automate telecom journeys such as paying bills, service inquiry, service upgradation, tracking usage, and more. Conversational AI can help telecoms understand customer sentiments and intent to drive self-service automation rates, thus reducing contact center costs. Should a customer need agent assistance, a CSA platform passes context from self-service to agents to create a frictionless interaction and a lower average handle time.

Agent Co-Pilot: CSA enables real-time analysis of customer context, including sentiment and intent, to help co-pilot agent performance with in-call coaching alerts. The platform gives agents insight to be more empathetic as well as to improve upselling or cross-selling. For example, the AI could suggest that the agent offers a triple or quadruple play bundle when a customer already has more than one service; this helps increase ARPU.

Post-Call Analytics: A CSA platform can automate post-call analytics on 100% of customer interactions. These include voice, email, and chat to understand reasons for customer churn and sales effectiveness. It also helps drive 100% compliance and identifies other core, customer, and agent-centric experience areas for planning and operational improvements. Telecoms can also get insights into how customers are using channels such as self-service as well as to identify potential points of friction in a customer journey to optimize customer experience.

Build stronger, lasting relationships
As consumers navigate their way through these challenging times, understanding and acting on customer conversations will be critical for telecoms to drive measurable and sustainable improvement in customer satisfaction and loyalty. Automation helps telecoms deliver the kind of frictionless experiences that consumers today demand while reducing operational costs.

Automating and improving the entire conversation—self-service, human-to-human, and after a call—using CSA is a winning way for telecoms to overcome immediate challenges and emerge more robust in a post-COVID world.

The post Transforming the telecom industry with Artificial Intelligence appeared first on Artificial Intelligence.

Artificial Intelligence is often defined as the ability of a machine to learn how to solve cognitive problems—a process akin to human intelligence. Within the realm of scientific methodology and laboratory interconnectivity, the most applicable way of thinking about AI is not that it seeks to replicate human reasoning precisely, but rather that it uses human reasoning as a model with the goal of supplementing and augmenting human observation and decision processes.

In this context, an AI algorithm must be trained to interpret available data and specified criteria to meet business objectives and drive operational decision making. AI further refines this process through an accumulation of relevant observations over time as both the accumulated data and business objectives are refined, allowing the understanding of the machine to approach that of human cognition.

The inherent diversity of equipment and systems involved in science and discovery is particularly conducive to the benefits of AI, where laboratory operations can be continuously optimized by this constant and cumulative refinement ability.

AI goes beyond our human capacity by never getting bored, never getting tired, while constantly observing and relentlessly improving. Complex classification through pattern recognition is where the technology is today and is rapidly growing towards a centralized lab intelligence system.

AI and laboratory complexity

As scientific discovery becomes more complex and demands on lab throughput increase, AI applied in the lab environment is helping to achieve both scientific and economic outcomes. Labs are already looking to understand system usage, automate quality processes, and optimize throughput and capital spend. Most enterprises are collecting some form of data to drive not only quality and expand capacity, but also process consistency and effectively plan future resource allocation.

Visionary companies are using this data for both scientific purposes and operational needs to predict output and assess how a process or team is performing. However, the diversity of equipment, coupled with the need for manual analysis of the data collected, means that high-quality data is not consistently available, and whatever data is available is often not helpful for decision-making.

AI, coupled with universal sensing capabilities, enables companies to realize these operational and financial benefits. Through high-quality and readily available insights, for example, AI enables simultaneous monitoring of all equipment usage in a lab and holistic tracking capacity.

Other similarly valuable data-producing capabilities include automatic real-time process bottleneck detection, sending users issue alerts, and detecting equipment operational anomalies. The outcomes of having such data include a proactive, conditional maintenance strategy which ensures production commitments are met in a timely fashion.

In this article we will focus on the use of AI for operational applications, specifically employing AI to augment the actionable operational insights available to a lab manager, operator, director, or executive.

Comprised of an extensive array of instrumentation and countless methods, the complexity of today’s lab requires an understanding of how each instrument behaves within a complex matrix of interactions.

The ability to monitor operations and continually provide increasingly sophisticated insights, is the core reason for introducing AI in the operational lab environment. Business models for scientific advancement are demanding higher levels of efficiency. Adopting powerful sources of information will become a necessary component of scientific productivity and is an inevitable next step in the creation of lab management systems that are so efficient, only AI will be able to produce them.

The hidden language of power

To effectively manage the growing complexity of the lab and achieve a uniform understanding of operations, AI must be fed universal and high-quality data. Relatively simple sensors that collect basic information such as power, humidity, and vibration can provide surprisingly rich information if correctly interpreted. Collectively, these various sensors can act as a type of nervous system, providing AI the means to understand instrument status, behavior, and utilization.

For example, as power is drawn by an instrument, the patterns of the current reflect the detailed operation of each component within the instrument. However, this pattern is complex and often incomprehensible using standard signal processing techniques. AI can learn to detect and decipher these subtle variations from complex signal profiles and provide vital insights into instrument activity and condition.

The beauty of using AI to interpret power is that it can provide sensitivity, accuracy, and universality—simultaneously looking deeply into the inner workings of any given instrument while conferring the ability to look broadly at all instrumentation in concert. Using this ubiquitous and seemingly mundane sensor, AI can actually learn to identify and interpret a hidden language within the signals to produce a surprisingly detailed understanding of behavior and health of any instrument.

Power is the “voice of the machine.” Every ripple and pattern in the power signature can be correlated to an operation state of the instrument, yielding insight into function, health, and usage.

Through an observation and training period, AI learns to recognize the discrete operational modes of an instrument, such as a measurement, a calibration, a test run, and so on. By determining what each mode of operation looks like, AI learns to recognize it in the future. Over time, AI fine tunes an ever-growing model of what is actually happening within the instrument and begins to understand the voice of the machine.

In the following example of a nextgen sequencing prep system, the power draw reflects the superposition of multiple components and behaviors which AI can decipher into discrete operations.

Once AI understands the machine’s typical operation, the system can then become predictive in nature. Over time, AI improves the internal model of the equipment and learns to identify more complex workings and deviations, even to the level of correlating precursors with future behavior, such as predicting when an instrument will fail and why. AI can even learn to develop a prescriptive ability, suggesting when maintenance must be performed and actions that must be taken to prevent failure.

Once AI has extracted operational states and system conditions, the information can be aggregated into visualization dashboards to provide insights for making data-driven decisions. For example, the following utilization heat map is used to identify and reallocate underutilized instruments.

AI can focus on any “metric of merit.” By understanding the activity of all processes, metrics can be built around the instrument. Valuable operational metrics include utilization, throughput, and capacity planning. AI’s ability to accurately measure instrument state increases the longer the system is connected to the instrument. Power monitor data analysis using non-AI (heuristic-based) methods has trouble accomplishing even basic running/idle utilization identification yielding an accuracy of 80%. AI routinely achieves accuracy of 98% or more.

The lab-wide intelligence system

The applicability is limitless as power is ubiquitous for nearly all instrumentation. By connecting all lab equipment to AI through this universal source of data, AI algorithms can provide an unprecedented view of lab operations in their entirety. The universal nature of power allows a laboratory of any scientific discipline—be it biology, physics, or any other discipline—to greatly benefit from such a holistic operational view.

The post Voice of the Machine: AI and the Language of Power appeared first on Artificial Intelligence.

The RAISE 2020 summit (Responsible AI for Social Empowerment) has brought issues around artificial intelligence (AI) to the centre of policy discussions. Countries across the world are making efforts to be part of the AI-led digital economy, which is estimated to contribute around $15.7 trillion to the global economy by 2030. India, with its “AI for All” strategy, a vast pool of AI-trained workforce and an emerging startup ecosystem, has a unique opportunity to be a major contributor to AI-driven solutions that can revolutionise healthcare, agriculture, manufacturing, education and skilling.

AI is the branch of computer science concerned with developing machines that can complete tasks that typically require human intelligence. With the explosion of available data expansion of computing capacity, the world is witnessing rapid advancements in AI, machine learning and deep learning, transforming almost all sectors of the economy.

India has a large young population that is skilled and eager to adopt AI. The country has been ranked second on the Stanford AI Vibrancy Index primarily on account of its large AI-trained workforce. Our leading technology institutes like the IITs, IIITs and NITs have the potential to be the cradle of AI researchers and startups. India’s startups are innovating and developing solutions with AI across education, health, financial services and other domains to solve societal problems.

Machine Learning-based deep-learning algorithms in AI can give insights to healthcare providers in predicting future events for patients. It can also aid in the early detection and prevention of diseases by capturing the vitals of patients. A Bengaluru based start-up has developed a non-invasive, AI-enabled technology to screen for early signs of breast cancer. Similarly, hospitals in Tamil Nadu are using Machine Learning algorithms to detect diabetic retinopathy and help address the challenge of shortage of eye doctors. For the COVID-19 response, an AI-enabled Chatbot was used by MyGov for ensuring communications. Similarly, the Indian Council of Medical Research (ICMR) deployed the Watson Assistant on its portal to respond to specific queries of frontline staff and data entry operators from various testing and diagnostic facilities across the country on COVID-19. AI-based applications have helped biopharmaceutical companies to significantly shorten the preclinical drug identification and design process from several years to a few days or months. This intervention has been used by pharmaceutical companies to identify possible pharmaceutical therapies to help combat the spread of COVID19 by repurposing drugs.

AI-based solutions on water management, crop insurance and pest control are also being developed. Technologies like image recognition, drones, and automated intelligent monitoring of irrigation systems can help farmers kill weeds more effectively, harvest better crops and ensure higher yields. Voice-based products with strong vernacular language support can help make accurate information more accessible to farmers. A pilot project taken up in three districts — Bhopal, Rajkot and Nanded — has developed an AI-based decision support platform combined with weather sensing technology to give farm level advisories about weather forecasts and soil moisture information to help farmers make decisions regarding water and crop management. ICRISAT has developed an AI-power sowing app, which utilises weather models and data on local crop yield and rainfall to more accurately predict and advise local farmers on when they should plant their seeds. This has led to an increase in yield from 10 to 30 per cent for farmers. AI-based systems can also help is establishing partnerships with financial institutions with a strong rural presence to provide farmers with access to credit.

An AI-based flood forecasting model that has been implemented in Bihar is now being expanded to cover the whole of India to ensure that around 200 million people across 2,50,000 square kilometres get alerts and warnings 48 hours earlier about impending floods. These alerts are given in nine languages and are localised to specific areas and villages with adequate use of infographics and maps to ensure that it reaches all.

The Central Board of Secondary Education has integrated AI in the school curriculum to ensure that students passing out have the basic knowledge and skills of data science, machine learning and artificial intelligence. The Ministry of Electronics and Information Technology (MeitY) had launched a “Responsible AI for Youth” programme this year in April, wherein more than 11,000 students from government schools completed the basic course in AI.

As AI works for digital inclusion in India, it will have a ripple effect on economic growth and prosperity. Analysts predict that AI can help add up to $957 billion to the Indian economy by 2035. The opportunity for AI in India is colossal, as is the scope for its implementation. By 2025, data and AI can add over $500 billion and almost 20 million jobs to the Indian economy.

India’s “AI for All” strategy focuses on responsible AI, building AI solutions at scale with an intent to make India the AI garage of the world — a trusted nation to which the world can outsource AI-related work. AI solutions built in India will serve the world.

AI derives strength from data. To this end, the government is in the process of putting in place a strong legal framework governing the data of Indians. The legislation stems from a desire to become a highly secure and ethical AI powerhouse. India wants to build a data-rich and a data-driven society as data, through AI, which offers limitless opportunities to improve society, empower individuals and increase the ease of doing business.

The RAISE 2020 summit has brought together global experts to create a roadmap for responsible AI — an action plan that can help create replicable models with a strong foundation of ethics built-in. With the participation of more than 72,000 people from 145 countries, RAISE 2020 has become the true global platform for the exchange of ideas and thoughts for creating a robust AI roadmap for the world.

The post Artificial intelligence solutions built in India can serve the world appeared first on Artificial Intelligence.

While Artificial intelligence (AI) can bring a lot of opportunities in social transformation, inclusion and empowerment in areas like healthcare, agriculture, education and smart mobility, among other sectors, we must also protect it from interfering in human intelligence, Prime Minister Narendra Modi said on Monday.

Speaking at the Responsible AI for Social Empowerment (RAISE) Summit 2020, he said, “We must protect the world against the weaponization of AI by non-State actors. While we discuss AI, let us have no doubts that human creativity and human emotions continue to be our greatest strength. They are a unique advantage over machines. Even the smartest of AI cannot solve mankind’s problem without blending with our intellect and empathy.”

He urged everyone to also think of how we will retain this intellectual age over machines and ensure that we take care so that human intelligence is always a few steps ahead of AI.

“We should think about how AI can help humans to increase their own capacities…AI will unlock the unique potential of each person, it will empower them to contribute more effectively to the society,” he said adding that algorithm transparency is key to establishing trust in how AI is used and it remains our collective responsibility to ensure it.

A big role for AI

Modi listed the sectors in which he envisions a big role for AI – agriculture, creating next-generation urban infrastructure, addressing urban issues like reducing traffic jams, improving sewage systems and laying energy grids, making disaster management systems stronger and solving the problem of climate change.

He suggested using AI to seamlessly bridge language barriers and preserve the diversity of languages and dialects. He also suggested using AI for knowledge sharing. The PM also wished India to become a global hub for AI and hoped many more Indians would start working on this in the times to come.

Data regulation

Speaking at the event, Reliance Industries Chairman Mukesh Ambani said he was confident that the government would introduce a sound data regulation framework to protect national resources of ‘digital capital’ and ensure data privacy.

He said the time is ripe, and the tools are ready to make India a world leader in Artificial Intelligence (AI), and to make AI work for all Indians, it is important to understand the epoch-changing importance of AI.

“The advent of modern Artificial Intelligence is comparable only to the birth of intelligent life on our planet. In the coming decades, the total non-biological intelligence will exceed the rational intelligence of the entire human population,” Ambani said.

Having said that he also said this does not mean that AI will replace the human mind. “It never can. It never will. Rather, AI and other associated technologies of the fourth Industrial Revolution will vastly expand our capacity to solve the most complex and pressing problems before India and the world.”

Specifically, AI will enable India to move faster towards achieving the ambitious goals set by the PM including transforming India into a high growth economy on its path to $5 trillion, making Indian industry and small businesses Atmanirbhar and dominating new global value, and modernising Indian agriculture and multiplying incomes of farmers.

IBM to set up CoE with GeM

Similarly, IBM CEO Arvind Krishna said that AI can solve deep societal problems in India, and the company is ready to work with the government in various projects.

At the same time, the company is also setting up a centre of excellence in partnership with government e-marketplace (GeM).

“Our goal is to apply the power of AI to improve usability and transparency and drive efficiency in cost saving in public procurement,” Krishna said.

As per studies done by Accenture, AI has the potential to raise India’s annual growth rate by 1.3 percentage points and add $957 billion to India’s economy by 2035.

According to official data, more than 38,700 stakeholders from academia, the research industry, and government representatives from 125 countries have registered to participate in RAISE 2020, organized by the government.

The post Artificial intelligence can bring opportunities, but protect it from interference in human intelligence: PM Modi appeared first on Artificial Intelligence.