Data science which is defined as the significant technology of current times has been a game-changer across various industries. In a world where high-level digitization of processes is surging, the generation of data is in volumes, giving rise to the popularity of data science technology and its tools. Such solutions when deployed, tend to drive more productivity and efficiency. With its innovative offering including Big Data, Data Mining, Machine Learning, Data Analysis, and Data Analytics, data science as a field has become the backbone of several business processes today.

We can observe the surge in demand for data science services in various fields with market researches estimating its potential growth in the near future. According to a market report, the big data market is projected to reach US$103 billion by 2027, up from US$49 billion in 2019. As of now, the market is anticipated to value US$56 billion in 2020.

It has also been predicted by several market reports that the data science platform market size is expected to grow from US$37.9 billion in 2019 to US$140.9 billion by 2024, at a CAGR of around 30 percent during the forecast period.

The increasing focus of organizations on ease of the use of methods to drive efficient business processes and address growing need to derive comprehensive insights from large datasets to gain a competitive edge is considered as one of the major key factors fostering the growth of the data science platforms market. Moreover, the enhanced inclination of enterprises toward data-intensive business strategies while embracing advanced technologies to create infinite opportunities for the vendors of the data science platform is also catalyzing the flourishment of data science as a field.

The impact of this market influence can be gauged through growing demands for skilled data scientists and engineers in different regions. Where data scientist has become the sexiest job of the century, we can see the perks one can get in attaining the title. In terms of salary, data scientists are the ones earning quite handsome amounts among others in countries like the US, China, UK, etc. This has increased competition among various aspiring candidates who strive to possesses innovative skills to thrive in the data science field.

The US being the leading hub for technological advancements encourages the lavish scaling of data science programs and opportunities. Outside of the US, Shanghai, Paris, Tokyo, London, and certain other big cities are serving with great scope for a data science career.

Moreover, data science is gaining momentum in India as well, as various startups are adopting innovations driven for data-technologies. Data science has a great scope in India. However, considering this as a relatively new field, it has been observed that very few have the right expertise required for data science. India is also facing a similar challenge. The country lags behind in comparison to the US, where the boot camps and universities are offering experimental and relevant pedagogy to learners.

Besides, numerous startups across India are seeking to monetize on specialized data scientists who possesses the right skills to fulfill their requirements. Currently, there is a need to advance the conventional IT roles including software development, testing, IT administrators, database managers, etc. across the country to harness better aspects of data and data science as well.

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Differentiation is the name of the game when marketing products to consumers.  Differentiation’s mandatory companion is performance. Both of these are driven by creative individuals who use the latest technological tools to create viable, commercial packages that will deliver desired attributes all the way to their expiration date.

However, there is room for improvement.  A gap exists between the “idea generators” and the “idea executors.”

What do we mean by that?

Let’s refer to the old telephone game.  I whisper something in your ear and you have to pass it along to the next person. After the information changes “ears” several times, the message being delivered to the final person almost always bears no resemblance to the original message.

We have personally experienced the same thing with bottle design. 

Imagine yourself in a meeting and instructing someone verbally what design or the modifications you want implemented as a 3D visual.  By the time it gets delivered to a CAD engineer, it has already been subject to interpretation and may not be at all what you envisioned.

Enter artificial intelligence (AI), which is typically defined as computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech and geometric vocabulary recognition, decision-making, and interactive feedback. 

We believe the plastics packaging sector can benefit from software that simplifies the “tools of creation,” and allows more people to join the party.  Think of it as something similar to PowerPoint in ease-of-use.  For example, a software program that gives you menu options which allow you to select bottle shape attributes such as a shoulder, label, grip and base.  As well as selecting more “building tools” such as “make that 28 mm finish, flare out the shoulder, give me a 0.3mm thickness and opt for a transparent appearance.”

Having this type of artificial-intelligence software available means that just about everyone in the decision chain (marketing, sales, production, etc.) can be empowered to contribute. Creativity now can be captured from all involved (not just the design engineer) to bring the bottle to life. Right now, only well-trained CAD operators can produce these renderings. 

We just want to make clear that we are not advocating that the basic software “tool” we are proposing be a substitute for the very expert and precise capability these professionals have.  They still need to take the concept and turn it into a fully engineered model.  We are simply saying that artificial intelligence-driven software can help improve communication, reduce iterations and encourage the involvement of more people, which we feel can improve the end result.

In short, integrating artificial intelligence into CAD programs can change the game for design because it can enable non-CAD users to more quickly and accurately design a package with desired attributes based on parameters built into the AI driven CAD system.

So, is anyone listening out there?  We’ve issued a challenge.  We hope someone who is reading this will consider taking it on.

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A major challenge in current climate prediction models is how to accurately represent clouds and their atmospheric heating and moistening. This challenge is behind the wide spread in climate prediction. Yet accurate predictions of global warming in response to increased greenhouse gas concentrations are essential for policy-makers (e.g. the Paris climate agreement).

In a paper recently published online in Geophysical Research Letters (May 23), researchers led by Pierre Gentine, associate professor of earth and environmental engineering at Columbia Engineering, demonstrate that machine learning techniques can be used to tackle this issue and better represent clouds in coarse resolution (~100km) climate models, with the potential to narrow the range of prediction.

“This could be a real game-changer for climate prediction,” says Gentine, lead author of the paper, and a member of the Earth Institute and the Data Science Institute. “We have large uncertainties in our prediction of the response of the Earth’s climate to rising greenhouse gas concentrations. The primary reason is the representation of clouds and how they respond to a change in those gases. Our study shows that machine-learning techniques help us better represent clouds and thus better predict global and regional climate’s response to rising greenhouse gas concentrations.”

The researchers used an idealized setup (an aquaplanet, or a planet with continents) as a proof of concept for their novel approach to convective parameterization based on machine learning. They trained a deep neural network to learn from a simulation that explicitly represents clouds. The machine-learning representation of clouds, which they named the Cloud Brain (CBRAIN), could skillfully predict many of the cloud heating, moistening, and radiative features that are essential to climate simulation.

Gentine notes, “Our approach may open up a new possibility for a future of model representation in climate models, which are data driven and are built ‘top-down,’ that is, by learning the salient features of the processes we are trying to represent.”

The researchers also note that, because global temperature sensitivity to CO2 is strongly linked to cloud representation, CBRAIN may also improve estimates of future temperature. They have tested this in fully coupled climate models and have demonstrated very promising results, showing that this could be used to predict greenhouse gas response.

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Not much time passes these days between so-called major advancements in artificial intelligence. Yet researchers are not much closer than they were decades ago to the big goal: actually replicating human intelligence. That’s the most surprising revelation by a team of eminent scholars who just released the first in what is meant to be a series of annual reports on the state of AI.

The report is a great opportunity to finally recognise that the current methods we now know as AI and deep learning do not qualify as “intelligent”. They are based on the “brute force” of computers and limited by the quantity and quality of available training data. Many experts agree.

The steering committee of AI Index, November 2017 includes Stanford’s Yoav Shoham and Massachusetts Institute of Technology’s Eric Brynjolfsson, an eloquent writer who did much to promote the modern-day orthodoxy that machines will soon displace people in many professions. The team behind the effort tracked the activity around AI in recent years and found thousands of published papers (18 664 in 2016), hundreds of venture capital-backed companies (743 in July 2017) and tens of thousands of job postings. It’s a vibrant academic field and an equally dynamic market (the number of US start-ups in it has increased by a factor of 14 since 2000).

All this concentrated effort cannot help but produce results. According to the AI Index, the best systems surpassed human performance in image detection in 2014 and are on their way to 100% results. Error rates in labelling images (“this is a dog with a tennis ball”) have fallen to less than 2.5% from 28.5% in 2010. Machines have matched humans when it comes to recognising speech in a telephone conversation and are getting close to at parsing the structure of sentences, finding answers to questions within a document and translating news stories from German into English. They have also learnt to beat humans at poker and Pac-Man. But, the authors of the index wrote:

Tasks for AI systems are often framed in narrow contexts for the sake of making progress on a specific problem or application. While machines may exhibit stellar performance on a certain task, performance may degrade dramatically if the task is modified even slightly. For example, a human who can read Chinese characters would likely understand Chinese speech, know something about Chinese culture and even make good recommendations at Chinese restaurants. In contrast, very different AI systems would be needed for each of these tasks.

The AI systems are such one-trick ponies because they’re designed to be trained on specific, diverse, huge data sets. It could be argued that they still exist within philosopher John Searle’s “Chinese Room”. In that thought experiment, Searle, who doesn’t speak Chinese, is alone in a room with a set of instructions, in English, on correlating sets of Chinese characters with other sets of Chinese characters. Chinese speakers are sliding notes in Chinese under the door, and Searle pushes his own notes back, following the instructions. They can be fooled into thinking his replies are intelligent, but that’s not really the case. Searle devised the “Chinese Room” argument — to which there have been dozens of replies and attempted rebuttals — in 1980. But modern AI is still working in a way that fits his description.

Machine translation

Machine translation is one example. Google Translate, which has drastically improved since it started using neural networks, trains the networks on billions of lines of parallel text in different languages, translated by humans. Where lots of these lines exist, Google Translate does okay — about 80% as well as an expert human. Where the data is lacking, it produces hilarious results. I like putting in Russian text and telling Google Translate it’s Hmong. The results, in English or Russian, will often be surprising — like the pronouncements found inside fortune cookies.

I doubt this is accidental. There are probably not many legitimate calls for translations from Hmong, so idle tricksters must have helped train Google’s translation machine to produce various kinds of exquisite nonsense.

Researchers are trying to overcome the data insufficiency problem. Two recently published papers show how machine translation can work based on monolingual data sets, using the statistical likelihood of certain words being grouped together. The quality is not as good as with bilingual training data, but it’s still not complete nonsense and workable in a pinch. These are, however, mere crutches that don’t change the general brute force approach.

Solving complex tasks requires ever more power and ever more data. A computer beat humans at Othello the year Sarle wrote about the Chinese Room and at poker this year — but that’s a quantitative leap rather than a qualitative one.

This kind of “artificial intelligence” continues to be a promising line of both research and business while there are growing quantities of “big data” to parse. Kai-Fu Lee of Chinese investment firm Sinovation Ventures, one of the experts who contributed essays to AI Index 2017, wrote that China was competitive against the US in artificial intelligence because it generates oodles of data:

In China, people use their mobile phones to pay for goods 50 times more often than Americans. Food delivery volume in China is 10 times more than that of the US. It took bike-sharing company Mobike 10 months to go from nothing to 20m orders (or rides) per day. There are over 20m bicycle rides transmitting their GPS and other sensor information up to the server, creating 20TB of data everyday. Similarly, China’s ride-hailing operator Didi is reported to connect its data with traffic control in some pilot cities. All of these Internet connected things will yield data that helps make existing products and applications more efficient and enable new applications we never thought of.

The data dependence, however, isn’t great for AI’s future development. A backlash against the limitless data collection is gathering strength in the West; nation states are putting up barriers to data sharing; the weaponisation of data sets to produce intentionally flawed results and flawed responses to them is not far off. And it’ll be far harder to detect than, for example, the weaponisation of social networks by Russian information warriors has been.

Meanwhile, the AI Index estimates that modern machines’ capacity for common sense reasoning is far less than that of a five-year-old child. Hardly any progress is being made in that area, and it’s hard to quantify.

An increasing capacity for data crunching can be both helpful and dangerous to humans. It isn’t, however, a game changer. And it’s up to us to keep this branch of computer science in its place by only giving it as much data as we’re comfortable handing over — and only using it for those applications in which it can’t produce dangerously wrong results if fed lots of garbage. The technology itself is not the kind that can push us away from the controls — entirely new approaches would be necessary to create that threat.

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