Artificial Intelligence (AI) has been redefining society in ways we have never anticipated. Technology is clinging to us in every walk of our lives, right from unlocking our smartphones to our day-to-day activities, online shopping, intelligent car dashboards, autonomous robots and so on. Though the concept of AI was first talked about in the early 1950s, forming a basis for many computer learning and complex decision-making processes, it is only of late, where processing huge amounts of data is required, that this field of technology is picking up pace.

What is in the AI basket?

AI is not a technology, rather it is a science or field of study. It is a constellation that encompasses a lot of statistical computation methods, pre and post analyses techniques for handling structured and unstructured data. It is an interesting endeavour of replicating and stimulating human intelligence through machine and deep learning platforms, natural language generation, virtual agents, text-voice-image recognition, AI optimised hardware, robotic process automation, cognitive search system and so on. It has a goal of utilising all the technologies to make intelligent machines.

Growth of AI in India

AI is the tool of innovation being experimented with, in almost all Indian domains, including healthcare, education, agriculture, finance, automobiles, energy, retail, manufacturing, scientific research with autonomous discoveries in place. In India, companies like Walmart, Google, Microsoft, Amazon, Samsung are into AI-based research and product offerings. Still, our country has a lot of potential to expand its research in this cutting-edge technology. Most of our educational, government and private institutes cradle and motivate AI researchers, innovations and start-ups.

The Government is pushing the private sector and offers many opportunities through DST, Niti Aayog, IndiaAI and many more, to create innovative technological solutions and fund AI-based start-ups. The start-ups are focussed in the cities like Bengaluru, Hyderabad, Ahmedabad, Mumbai, Delhi for AI-based businesses.

Why AI matters in today’s scenario

AI, which emerged from the research world as a proof-of-concept has been strategically scaling up due to the pace of digitisation. AI is favoured for its large data processing, end-to-end efficiency of decoding complex processes, improved accuracy and help in decision-making, intelligent offerings, smart services – content, task automation and so on. We can see its overwhelming development in healthcare, pharmaceutical, scientific research, and e-commerce.

The interactive applications of Google, DeepMind’s Alpha Fold, BenevolentAI, chatbots such as Clara and Zini; Aryoga Setu, Co-Win, Amazon, Zomato, Swiggy are among the few proving to be our pandemic tech saviours.

Impact of AI in business

Business over the years has evolved from local corner shops to the booming online shopping platforms. These modernised techniques not only make individual lives easier, but also streamline business processes for improving consumer experience, sales forecasting and automated decision making to meet business goals. Businesses work well when humans, machines and technologies integrate for each other’s benefit. Today’s business world is solely dependent on AI, Cloud, Big Data technologies of which e-commerce and m-commerce are the mainstream, having a great business impact globally. In synch with global developments in innovation and automation, India too has brought about a digital transformation over the last two decades. Now, technological developments have gained pace more than what has been predicted; the pandemic played a great role in its quick transformation and adoption.

How to look for jobs in AI?

Today, Artificial Intelligence is a lucrative domain, promising job growth in a competitive IT industry. Four out of five C-suite executives believe that they need to speed up data processing and automation, if they have to survive in their business. So, recruiters look for advanced technical skills, extensive practical experience. AI skills secure the top place among the fastest growing job profiles over the recent years. The prominent job roles include big data engineer, business intelligence developer, data scientist, data analyst, cyber analyst and expert, AI-Deep learning-machine learning engineer, computer vision specialist along with equivalent research jobs.

How can one find a good job in AI? The answer is, there are several avenues and opportunities to be had by connecting with experts via LinkedIn, technical blogs, career fairs and company career sites. The tech talks given by companies in university, conclaves hosting academics-government-industry groups will help you understand the actual employment needs and goals. Always aim to seek opportunities at government and industry-funded research labs during the early years of your higher education. This will help you to nurture your skillset to the best. Work for open-source and stack overflow contributions which will add value to your technical profile. Technical competitions like hackathons/ideathons/makeathons will upskill your innovative ideas along with the required life skills.

Globally, today we are in a challenging situation. All, irrespective of the sectors, are working on the revamp strategy to balance the economy post Covid-19. AI will endeavour to revive the profitability and development of industry . New and advanced opportunities are expected to open up.

AI is and will be driving a promising future in the new normal. It will be the main driver for emerging and new technologies. So, take an interdisciplinary approach to hone your skills in an ever-evolving field. Think big, start small, act fast.

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Leveraging artificial intelligence (AI) is now commonplace in marketing. Tools, platforms, and services put sophisticated audience targeting and segmentation tools at marketers’ fingertips, making it easier than ever to connect your products and services to customers.

As organizations grow more sophisticated in their adoption of AI, they can start to look beyond core uses cases and solutions such as the basic targeted advertising tools offered by Google and Facebook. Today, consistently finding new applications of data and smart algorithms is essential to building and maintaining a competitive advantage.

It’s important because if you’re simply using AI to do the same as everyone else then your results won’t differentiate you from your competitors in the eyes of potential customers. On top of this, core AI marketing tools (such as Adwords or Facebook advertising) are often described as “pay-to-win” – put simply, companies with bigger marketing budgets will generally get better results and take potential customers from smaller businesses. This is because they can afford to bid higher for the most important keywords.

Thinking smarter about alternative and emerging use cases for AI in marketing (or any business function) can help mitigate this. Here are a few ideas for getting ahead of the curve and ensuring your AI-driven marketing strategy is always evolving to become more efficient.

Intelligent advertising design

AI enables highly personalized design, meaning individual elements of advertising materials and marketing campaigns can be automatically tailored to specific audiences or even individuals. Individual elements of marketing campaigns – down to the style of design or the color schemes used – can be determined by algorithms to ensure they have the best likelihood of grabbing your audience’s attention and prompting further engagement. Algorithms can then assess the performance of different combinations of design elements and audiences and determine where tweaks could bring better results.

Thinking smarter about alternative and emerging use cases for AI in marketing (or any business function) can help mitigate this. Here are a few ideas for getting ahead of the curve and ensuring your AI-driven marketing strategy is always evolving to become more efficient.

Intelligent advertising design

AI enables highly personalized design, meaning individual elements of advertising materials and marketing campaigns can be automatically tailored to specific audiences or even individuals. Individual elements of marketing campaigns – down to the style of design or the color schemes used – can be determined by algorithms to ensure they have the best likelihood of grabbing your audience’s attention and prompting further engagement. Algorithms can then assess the performance of different combinations of design elements and audiences and determine where tweaks could bring better results.

Another powerful use case is trend analysis. Here AI can help you pick out changing habits and behaviors that might influence how your customers and potential customers are engaging with providers in your market. As well as your own visual messaging, you can more easily assess the effectiveness of your competitors’ campaigns and judge how customers react to different moods, color palettes, and scenery.

Image recognition (along with NLP as mentioned above) can also be used to create automated descriptions for sales copy, from pictures of items. Additionally, you can use it to protect your brand, by having it automatically alert you to anyone who might be misappropriating your creative IP, branding, or messaging for their own ends!

Try-before-you-buy with AR

Ikea lets customers view products in their own homes – to check how a new sofa or table might fit with their existing décor – by offering augmented reality (AR) tools that superimpose computer-generated graphics onto real-world images. Here, AI is used to create realistic-looking composite images, generally in real-time, as the user is looking through the camera on their phone. In the same manner, beauty brands such as L’Oreal let users try out make-up and other products and see how they will look on them, using the same technology.  While big players have been making this sort of functionality available to their customers for a while, it is increasingly offered “as-a-service” through platforms such as wearfits.com that make it usable by retailers of any size. 

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Multiple industries across the world are embracing the functionalities of Artificial Intelligence to boost productivity and gain high ROI. Law enforcement officers have also started utilising Artificial Intelligence in solving crimes— from petty to horrendous. The machine learning models, help the police department to guide through each step without wasting too much time on any wrong process to capture criminals. It boosts productivity to help victims get justice as soon as possible. Jurors consider multiple pieces of evidence captured by Artificial Intelligence to decide the severity of the punishments. Thus, there is a significant rise in crime prevention and detection after using Artificial Intelligence in solving crimes.

Cities are getting smarter with the continuous installation of hi-tech CCTVs and buying smart devices. Artificial Intelligence models work on the real-time information that comes from these CCTVs and records from smart devices. Whenever police officers need to track criminals down, they prefer to use AI like a smart assistant to monitor the movements precisely. The facial recognition system helps in understanding the last location of any victim as well as detecting the behaviour of the criminal or abductor.

AI also helps to detect gunfire shots with sensors connected to a cloud-based application. It does not need any passer-by to call the police and inform them about the gunfire. The multiple sensors capture the time and sound of gunfire shots and send real-time data to the police department for determining the current position of the shooter.

Artificial Intelligence, is solving crimes by detecting essential and hidden clues from multiple crime scenes that are impossible to detect with the naked human eye. It opens a plethora of connections to new links from the evidence from photographs. The new links are worth checking out because it reduces the time to solve the crime and get hold of the criminal involved.

The appropriate use of Artificial Intelligence in solving crimes, helps the law enforcement officers in crime prevention and detection efficiently. Though there are some potential risks in using AI and machine learning models, it is becoming popular across the world.

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The demise of big data and the emergence of smart data are helping comply with demands

The era of bigdata has come to an end. Data has been a vital component of creating strong, long-lasting, mutually beneficial relationships between businesses and customers for a long time. They provided a way for business executives to better understand the customers and fulfill their needs based on previous preferences. But very quickly, the night has changed. We have already bid farewell to bigdata in business, so what next? Yes, it is smart data. The emergence of smart data has leveraged a stronghold on companies to equip them to the improving demand.

‘Bigdata,’ the term that opens the door to over 800 million results in Google search has been ruling over us for the past two decades. Starting from offering a cost-efficient means of insight to optimizing return-on-investment and growth, bigdata in business has played a significant role. Unfortunately, all along the way, marketers had a hard time solving the riddles of data. Whether it is accumulating the right data, organizing it so it can be easily analyzed, or being able to extract useful insights, there are a number of challenges that data engineers are undertaking to extract insight. The technology sector is evolutionary. While some technologies garnered peak popularity at one instance of time, they gradually went on to evolve after being perceived that their data were over. The same thing is happening to bigdata. As the technology has reached a form of saturation, it is turning to be smart data. Data scientists have long been complaining about spending around 80% of their time cleansing, verifying, and preparing data. Fortunately, as smart data comes in handy with a well cleansed, verified and fertilized mechanism, the world finds it easy to handle.

Cremating big data

Bigdata is a great marketing term, but in reality, that’s all it is. The enormous amount of data is nothing useful without getting good insight from it. As companies become more familiar with data processing and service providers abstract away more complexity, bigdata in business will just become big. But data is not dead. It is just transforming to an easier form. By 2025, it is predicted that the global data sphere will be 175 zettabytes, up from 50 zettabytes in 2020. Henceforth, we are preparing for a future where the data explodes exponentially and we have sources to store and get the best out of it.

Why is bigdata becoming useless? Let me explain this with an example. Recently, the volume of data is drastically growing. People also rely on data for many situations. According to a survey of Fortune 1000 executives by the Harvard Business Review, reliance on bigdata initiatives is on the rise. Unfortunately, there are some cases where data reliance has impacted badly on certain scenarios. In a data wrong gone incident, the portal was using more data than was called for. OfficeMax has sent a letter to an individual in Illinois, addressed to Mike Seay. The letter reported the death of his daughter in a car crash. But the worst case was that Seay’s daughter was killed in a car crash one year earlier. The scenario was even ruined as Seay was on his way to attend a counseling group of grieving parents when he received and read this letter.

The rise of smart data

Smart data is digital information that is formatted so it can be acted upon at the collection point before being sent to a downstream data analytics platform for further data consolidation and analytics. The term smart data analytics is associated with the Internet of Things (IoT) and most of the data is extracted from smart sensor-embedded devices. To get the maximum out of smart data, one has to better understand the clues in the question around data. Besides from making data-driven decisions, smart data analytics pushes us to make creative initiatives.

Data analytics were dependent on the famous Vs (Velocity, Variety, and Veracity) that big data carried. However, smart data also revolves around Veracity and Value. With smart data, we focus on valuable data and often smaller datasets that can be turned into actionable data and effective outcomes to address customer and business challenges. By putting in the context of purpose and context, smart data analytics makes the analysis and interpretation of data easy. For example, smart data is used in Open banking where UK-regulated banks have to give customers the option to allow access and control of their personal and financial data to TPPs.

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Blueshift raises $30 million in Series C funding round to scale its SmartHub customer data platform

Marketing and customer experience (CX) are increasingly intertwined in today’s connected world, and marketers are being tasked with understanding customers through the lens of CX data to craft personalised experiences. However, traditional marketing platforms focus only on marketing response data (like clicks), and are unable to leverage CX data from across the customer journey. The first generation of customer data platforms (CDPs) attempted to solve this challenge by focusing only on data integration, but lacked any intelligent decisioning, and were not built for marketers or CX professionals.

Blueshift’s SmartHub CDP platform combines the data fidelity of a CDP with the intelligence needed for marketers to make real-time decisions. This enables them to not only unify 360-degree CX data (CDP), but also to make AI-powered decisions from large volumes of data (Smart), and distribute the decisioning to every touchpoint in the customer journey (Hub). The Blueshift SmartHub CDP platform uses patented AI technology to unify, inform, and activate the fullness of customer data across all channels and applications. Put simply, it gives brands the tools they need to deliver 1:1 experiences in real-time across the customer journey.

Blueshift’s SmartHub CDP platform has been adopted by global brands including LendingTree, Discovery Inc., Udacity and BBC, and has been shown to deliver 781% RoI in a study conducted by Forrester Research. “With the increased urgency towards digital transformation, we have seen an increased demand for a SmartHub CDP, that can not only unify silo-ed data, but also unify silo-ed experiences,” said Vijay Chittoor, co-founder and CEO of Blueshift.

Recently, Blueshift announced a $30 million Series C funding round, bringing the total amount raised to $65 million. This funding round was led by Fort Ross Ventures, along with Avatar Growth Capital. Existing investors including Softbank Ventures Asia, Storm Ventures, Conductive Ventures and Nexus Venture Partners also participated in the round.

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Campbell Brown is CEO & Co-Founder of PredictHQ – Demand Intelligence for a dynamic world. Kiwi, family man, lover of travel, data & APIs. 

For more than a decade, big data has been steadily soaring. New data-driven companies have emerged and become multibillion-dollar juggernauts, while established market leaders recognized the power of data early and have invested accordingly. But like with so many things, 2020 was a wake-up call for data strategies, especially the many not delivering immediate value.

I think it’s time that we accept the limitations of big data and embrace the need for smart data. The shift toward smart data has been going on for at least a decade. The central idea behind my own company is to equip companies with the smart data they need to improve their demand forecasting.

While accurate external data is one of the few factors that can bring certainty to your planning, many data scientists report spending around 80% of their time cleansing, verifying and preparing data. This new era of smart data—data that is already cleaned, verified, featurized and able to be plugged into a model and start delivering value swiftly—is rich in possibility. Smart data is configured to enable models to find and use the most impactful data faster, so it learns how the world works and makes better decisions. The companies that make the most of smart data will likely be those that shake off the big-data ways of thinking ASAP.

How Did We Get To Drowning In Big Data?

For decades, companies have poured money into data sources and pumped more information into their data lakes. But new data does not always equal new intelligence—to machine learning models and especially to core business strategies—and slowly the weight of all this data has built up.

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Because most data sources need cleansing and standardizing, expensive employees wielding multiple postgraduate degrees have often found themselves spending most of the week tidying up data. This coupled with the complexities of producing useful machine learning models when nearly 90% of data science projects never enter productionmeans that a lot of data science investment hasn’t amounted to big wins for the bottom line.

Enter the pandemic. As finance teams ran the ruler over everything, any nice-to-haves had to go. The right data projects are make-or-break, but anything that couldn’t be used to improve core functions such as demand forecasting, pricing or driving competitive advantage couldn’t be justified. The more-data-is-better era is over—it is time for a new era of smart data.

Aggregating And Using Data At Scale Is Only Half The Answer 

The most important part is extracting value. How do you make your data work for your goals, right now? The analysis paralysis of the last 10 years has to end.

Big-data strategies have been trying to boil the ocean for too long. These maximalist approaches don’t work because they’re way too hard. They never did, and they definitely don’t when you have fewer team members, tighter margins and your demand forecasts rely on last year’s data. From what I’ve seen, the best businesses now are focusing less on the depth and breadth of their data lake and more on getting the most value out of it.

It’s time to flip our data strategies from paralyzing to enabling—to take that asset and turn it into something you can get value from. Right now. Set your data scientists free to do the work they dream about: not collecting, aggregating and cleaning, but building models to tap into signals over noise for core processes such as labor optimization and price forecasting.

McKinsey’s chief data officer and one of the company’s partners put it well in an article from February: “Many companies have made great strides in collecting and utilizing data from their own activities. So far, though, comparatively few have realized the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. … The COVID-19 crisis provides an example of just how relevant external data can be. In a few short months, consumer purchasing habits, activities, and digital behavior changed dramatically, making preexisting consumer research, forecasts, and predictive models obsolete. Moreover, as organizations scrambled to understand these changing patterns, they discovered little of use in their internal data. Meanwhile, a wealth of external data could — and still can — help organizations plan and respond at a granular level.”

As you look forward, you can use data to inform dynamic decision making and make models more accurate, providing certainty in the Covid chaos. You can use data to enable smart decisions about efficiencies and opportunities hiding in plain sight, and you can do that immediately rather than after three months of a team of five data scientists working on it. This is the power of smart data.

What Defines Smart Data?

As companies learn to do more with less, automation and machine learning become critically important. Smart data starts with reliable and verified data, but it’s more than just a record of truth. It needs to be enriched, contextualized and featurized so that it’s no longer simply raw information (however high-quality). This reduces the friction and error-prone nature of feeding new data into machine learning models.

As you are assessing every new data source, you should be asking of it:

• Does this data provide adequate depth to provide enough context for the issues I am building models to solve?

• How verified and accurate is the data? 

• How frequently is it updated and re-verified as we head into a long-awaited but chaotic recovery?

• Is it enriched enough that my models will know what to do with its input?

• Which core business decisions will this enable my machine learning models to make better and faster? 

• Can I easily explain what this data source is showing, and its impact on my forecasts and operations?

Last year left many companies flat-footed by the ultimate black swan event. Many are now working hard to integrate new data and roll out data-driven recovery plans to know what will drive their demand. Whether it’s a televised sports game or festival driving up demand, a flood or terrorist attack driving down demand, or clusters of smaller events bringing people at a scale unforeseen by most companies, many companies are on it. And I think every company needs to be.

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Researchers have used smart speakers to measure the individual heartbeats of people in the same room without the need for any physical contact.

A team from the University of Washington found that by sending inaudible sounds from the speaker out into a room, heartbeats can be measured based on the way the sounds are reflected back to the speaker.

As the heartbeat is such a tiny motion on the chest surface, machine learning was used to help the smart speaker locate signals from both regular and irregular heartbeats.

When the system was tested on healthy participants and hospitalised cardiac patients, the smart speaker detected heartbeats that closely matched the beats detected by standard heartbeat monitors.

“Regular heartbeats are easy enough to detect even if the signal is small, because you can look for a periodic pattern in the data,” said co-senior author Shyam Gollakota.

“Irregular heartbeats are really challenging because there is no such pattern. I wasn’t sure that it would be possible to detect them, so I was pleasantly surprised that our algorithms could identify irregular heartbeats during tests with cardiac patients.”

While many people are familiar with the concept of a heart rate, doctors are more interested in the assessment of heart rhythm. Heart rate is the average of heartbeats over time, whereas a heart rhythm describes the pattern of heartbeats.

For example, if a person has a heart rate of 60 beats per minute, they could have a regular heart rhythm – one beat every second – or an irregular heart rhythm, with beats randomly scattered across that minute but still averaging out to 60 beats per minute.

“Heart rhythm disorders are actually more common than some other well-known heart conditions. Cardiac arrhythmias can cause major morbidities such as strokes, but can be highly unpredictable in occurrence and thus difficult to diagnose,” said researcher Dr. Arun Sridhar.

“Availability of a low-cost test that can be performed frequently and at the convenience of home can be a game-changer for certain patients in terms of early diagnosis and management.”

The key to assessing heart rhythm lies in identifying the individual heartbeats. For this system, the search for heartbeats begins when a person sits within one or two feet of the smart speaker.

The system then plays an inaudible continuous sound, which bounces off the person and returns to the speaker. Based on how the returned sound has changed the system can isolate movements on the person, including the rise and fall of their chest as they breathe.

This algorithm combines signals from all of the smart speaker’s multiple microphones to identify the elusive heartbeat signal while isolating it from other factors such as the person’s breathing.

“This is similar to how Alexa can always find my voice even if I’m playing a video or if there are multiple people talking in the room,” Gollakota said. “When I say, ‘Hey, Alexa,’ the microphones are working together to find me in the room and listen to what I say next. That’s basically what’s happening here, but with the heartbeat.”

Currently the system is set up for spot checks: if a person is concerned about their heart rhythm, they can sit in front of a smart speaker to get a reading.

The research team hopes that future versions could continuously monitor heartbeats while people are asleep, something that could help doctors diagnose conditions such as sleep apnea.

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As data centers become enabler to a nation’s economy, employing artificial intelligence can yield higher benefits

Artificial Intelligence (AI) plays a pivotal role in capturing, processing, and analyzing data at much faster rate than ever, today! It is also becoming more efficient and useful to incorporate data elements and managing data centers.

With data becoming a pre-requisite to sustain almost every business operation for insight and business results, data centers are on the crux of this digital transformation. These physical facilities that house the computers and equipment power the information needs of the modern economy. Data centers provide seamless data backup and recovery facilities while supporting cloud storage applications and transactions. Apart from boosting economy, the data center ecosystem attracts many international tech companies for the nation. Moreover, the presence of data centers ensure an excellent investment climate and employment opportunities for the local community.

Despite their key role in bringing digital revolution, they are not without problems. According to Gartner analyst Dave Cappuccio, 80% of enterprises will shut down their traditional data centers by 2025. The figures are fitting considering the host of problems faced by traditional data centers like lack of readiness to upgrade, infrastructure challenges, environmental issues and more. And the remedy for this is leveraging artificial intelligence to enhance the data center functions and infrastructure.

As per a Forbes Insights report, in early 2020, artificial intelligence is poised to have a tremendous impact on data center management, productivity, and infrastructure. Meanwhile, its technologies continue to offer data centers’ potential solutions to improve operations over the long term. In return data centers enabled by accelerated computing capabilities of AI, would be able to process AI workloads more efficiently.

Data centers consume a lot of energy, so training an artificial intelligence network to improve power usage effectiveness (PUE) is a key goal. PUE is essential metric to measure data center efficiency. In 2014 by deploying Deepmind AI in one of its facilities, Google was able to consistently achieve a 40% reduction in the amount of energy used for cooling, which equated to a 15% reduction in overall PUE overhead after accounting for electrical losses and other non-cooling inefficiencies. It also produced the lowest PUE the site had ever seen. Deepmind analyzes over 100 different variables within the data center to improve efficiency and reduce power consumption.

Data centers are also susceptible to various cyber threats. Cybercriminals are always finding new ways to obtain data from data centers or launch their next data breach attack. By learning normal network behavior and detecting cyber threats based on deviation from that behavior, artificial intelligence proves to be resourceful again!  Artificial algorithms can complement current Security Incidents and Event Management (SIEM) systems, by analyzing incidents and inputs from multiple systems, and devising an appropriate incident response system.

In a data center, IT devices are often deployed or removed from shelves that brings a lot of fragmented resources, like U space, which cannot be monitored or managed, and are easy to get wasted. By using intelligent hardware and IoT sensors, artificial intelligence allows effective data center infrastructure management that keeps a close eye on the data center and reduces repetitive work through automation. Here, data center managers can automate activities like temperature management, equipment status monitoring, floor security, fire hazards mitigation, ventilation, and cooling systems management. Coupled with predictive analytics, automation also helps in predictive maintenance at data centers.

Further, this AI-based predictive analysis can help data centers distribute workloads across the many servers in the firm. As a result, it will be easy to predict and manage data center loads more efficiently. It will also help in optimizing server storage systems, finding possible fault points in the system, improve processing times and reducing risk factors much faster.

Recently, MIT researchers had developed an AI system that automatically learns how to schedule data-processing operations across thousands of servers. This system was observed to be about 20 to 30% faster, and twice as fast during high-traffic times in completing key data center tasks. The researchers asserts that this artificial intelligence system could enable data centers to handle the same workload at higher speeds, using fewer resources.

Additionally, through deep learning (DL) applications, AI can predict failures and outages ahead of time. E.g.  HPE artificial intelligence predictive engine helps in identifying and resolving bottlenecks in the data center.  A survey of 200 companies highlighted that downtime results in losses surpassing US$26.5 billion, with the cost per minute of a network outage reaching approximately US$7,900. By monitoring server performance, network congestions, and disk utilization, AI can detect and predict data outages. Besides, it can implement mitigation strategies to help the data center recover from the data outage – thus adding to customer satisfaction and minimal losses during such outages.

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In the Harry Potter series, evoking even the name of the villain — Voldemort — spreads terror. In real life, Voldemort doesn’t exist. But simple words can still be enough to provoke mental instability, or even a panicked fear. Such is the case today for the term Artificial Intelligence, AI for short.

The phrase covers a range of incredibly effective tools, but also evokes such strong emotions of excitement and fear that people forget what it is in the first place — and at the risk of causing errors, blockages and frenzies. It is therefore essential that we stop talking about AI, assuming it isn’t too late.

When researchers invented the tools of Information Technology, they also created the vocabulary we use to refer to them. To believe the Robert dictionary, the word “computer” made its first appearance in text in 1955. The first “internet” reference came 40 years later, in 1995. But “Artificial Intelligence” comes from a combination of two old terms, both of which have a strong significance.

Initially, it was a marketing move. In 1995, four U.S. universities sent out invitations for a research seminar, held the following year at Dartmouth College, to conduct a “study on Artificial Intelligence,” assuming that “every aspect of learning or any other characteristic of intelligence can, in principle, be described so precisely that it is possible for a machine to simulate.” The goal was to attract researchers and funding, but the name stuck.

Recently, experts have put forward other suggestions. Joel de Rosnay, a specialist in future trends, proposes “auxiliary intelligence.” The researcher Luc Julia, director of Samsung’s Laboratory of Artificial Intelligence, prefers “augmented intelligence.” And consultant Pierre Blanc likes “algorithmic computing.”

Blanc is right to want to replace the word “intelligence,” which is what poses the main problem.

Intelligence has long been considered a distinctive trait of humanity. In the 17th century (again according to the Robert dictionary) the word was employed to designate a “human being as a thinking being, capable of reflection.” With artificial intelligence, a machine is supposed to acquire this human capacity. It could distinguish, discuss, and even decide, like HAL 9000; the famous computer from Stanley Kubrick’s 2001: A Space Odyssey, released in 1968.

Machines could, therefore, supplant man not just in physical capability (as has been the case for centuries) but also intellectually. According to an Ipsos survey for BCG Gamma, 50% of French and German people fear the effects of AI on their jobs, as do 47% in the United States, 45% in Britain, and 38% in Spain.

A powerful tool

For the moment, AI remains a myth. The concept presented in that Dartmouth seminar has yet to materialize. Machines “know,” of course, how to beat the world’s most capable humans at StarCraft II, Jeopardy!, or the Chinese game of Go. But these are extremely narrow competencies, which devour infinitely greater amounts of energy than is needed by the human brain. The most powerful machines in the world are like mathematical geniuses incapable of stopping someone in the road to ask directions.

And often, hidden behind artificial intelligence, is human stupidity — as Microsoft demonstrated last year with its chat software Tay, which was disconnected from Twitter due to horrific sexist and racist content less than one day after being put in service. Or by Amazon in 2018, with its fully automated recruitment system that automatically eliminated women.

So what is really behind what we conveniently call Artificial Intelligence? The truth is simple: it’s a combination of the internet and the computer! The computer, with an information processing capacity that has grown for half a century at the exponential rate of Moore’s Law (the density of transistors on a chip double every two years). And the internet, with its colossal capacity to gather and transmit data. As spelled out by Michel Volle, co-president of the Institute of Economic and Statistical Training: “Artificial Intelligence = Statistics + Computing”

Short and sweet, this equation still needs one further point to be completed: The calculating power and the mountains of data permitted by forms of automated learning (“machine learning” and then “deep learning”). This is how researchers were able to make great strides for a good decade in the matter of visual and vocal recognition. They will surely make more spectacular progress in the years to come.

And yet, what we call Artificial Intelligence is still nothing but a tool. A tool of fantastic power that will transform how businesses are organized, but a tool nonetheless. It’s a “technological platform,” explains economists Darren Acemoglu and Pascal Restrepo, that “could be deployed not just to automate, but also to reorganize production to create new heights of human productivity.” But here too, Artificial Intelligence will only do that which human intelligence decides.

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