We gauged the perspectives of experts in data science, asking them about the biggest emerging trends in data science

As one of the fastest evolving areas of tech, data science has seen a rise up the corporate agenda as less and less leaders base business decisions on guess work. With added capabilities such as artificial intelligence (AI) and the edge complementing the work of data scientists, the field is becoming more accessible to employees, but this still requires training of data skills, on the most part. In this article, we explore some key emerging trends in data science, as believed by experts in the field.

Increased involvement of AI and ML

Firstly, it’s believed that the involvement of AI and machine learning (ML) will increase further, and enable more industries to become truly data-centric.

“As businesses start to see the benefits of artificial intelligence and machine learning enabled platforms, they will invest in these technologies further,” said Douggie Melville-Clarke, head of data science at Duco.

“In fact, the Duco State of Reconciliation report – which surveyed 300 heads of global reconciliation utilities, including chief operating officers, heads of financial control and heads of finance transformation – found that 42% of those surveyed will investigate the use of more machine learning in 2021 for the purposes of intelligent data automation.”

Data science in insurance

Melville-Clarke went on to cite the insurance industry, often perceived as a sector that’s had difficulty innovating due to high levels of regulation, as an example for future success when it comes to data science.

He explained: “The insurance industry, for example, has already embraced automation for processes such as underwriting and quote generation. But the more valuable use of artificial intelligence and machine learning is to increase your service and market share through uses like constrained customisation.

“Personalisation is one of the key ways that banks and insurance companies can differentiate themselves, but without machine learning this can be a lengthy and expensive process.

“Machine learning can help these industries tailor their products to meet the individual consumers’ needs in a much more cost-effective way, bettering the customer experience and increasing customisation.”

The evolution of hyperautomation

Along with rising use of AI and ML models, organisations have been combining AI with robotic process automation (RPA), to reduce operational costs through automating decision making. This trend, known as hyperautomation, is predicted to help companies to continue innovating fast in a post-COVID environment in the next few years.

“In many ways, this isn’t a new concept — the key goal of enterprise investment in data science for the past decade has been to automate decision-making processes based on AI and ML,” explained Rich Pugh, co-founder and chief data scientist at Mango Solutions, an Ascent company.

“What is new here is that hyperautomation is underpinned by an ‘RPA-first’ approach that can turbocharge process automation and drive increased collaboration across analytic and IT functions.

“Business leaders need to focus on how to harness enterprise automation and continuous intelligence to elevate the customer experience. Whether that is embedding intelligent thinking into the processes that will drive more informed decision making, such as deploying automation around pricing decisions to deliver a more efficient and personalised service, or leveraging richer real-time customer insights in conjunction with automation to execute highly relevant offers and new services at speed.

“Embarking on the hyperautomation journey begins with achieving some realistic and measurable future outcomes. Specifically, this should include aiming for high-value processes, focusing on automation and change, and initiating a structure to gather the data that will enable future success.”

SaaS and self-service

Dan Sommer, senior director at Qlik, identified software-as-a-service (SaaS) and a self-service approach among users, along with a shift in advanced analytics, as a notable emerging trend in data science.

“To those in the industry, it’s clear that SaaS will be everyone’s new best friend – with a greater migration of databases and applications from on premise to cloud environments,” said Sommer.

“Cloud computing has helped many businesses, organisations, and schools to keep the lights on in virtual environments – and we’re now going to see an enhanced focus on SaaS as hybrid operations look set to remain.

“In addition, we’ll see self-service evolving to self-sufficiency when it comes to effectively using data and analytics. Empowering users to access data, insights and business logic earlier and more intuitively will enable the move from visualisation self-service to data self-sufficiency in the near future.

“Finally, advanced analytics need to look different. In uncertain times, we can no longer count on backward-looking data to build a comprehensive model of the future. Instead, we need to give particular focus to, rather than exclude outliers – and this will define how we tackle threats going forward too.”

Data fabric

With employees gradually becoming more comfortable with using data science tools to make decisions, while aided by automation and machine intelligence, a concept that’s materialised as a hot topic for the next stage of development is the concept of ‘data fabric’.

Trevor Morgan, product manager at comforte AG, explained: “A data fabric is more of an architectural overlay on top of massive enterprise data ecosystems. The data fabric unifies disparate data sources and streams across many different topologies (both on-premise and in the cloud), and provides multiple ways of accessing and working with that data for organisational personnel, and with the larger fabric as a contextual backdrop.

“For large enterprises that are moving with hyper-agility while working with multiple or many Big Data environments, data fabric technology will provide the means to harness all this information and make it workable throughout the enterprise.”

New career paths and roles

Another important trend to consider regarding the future of data science is the new career paths and jobs that are set to emerge in the coming years.

“According to the World Economic Forum (WEF)’s Future of Job’s Report 2020, 94% of UK employers plan to hire new permanent staff with skills relevant to new technologies and expect existing employees to pick up new skills on the job,” said Anthony Tattersall, vice-president, enterprise, EMEA at Coursera.

“What’s more, WEF’s top emerging jobs in the UK — data scientists, AI and machine learning specialists, big data and Internet of Things — all call for skills of this nature.

“We therefore envision access to a variety of job-relevant credentials, including a path to entry-level digital jobs, will be key to reskilling at scale and accelerating economic recovery in the years ahead.”

The ‘Industrial Data Scientist’

In regards to new roles to emerge in data science, Adi Pendyala, senior director at Aspen Technology, predicts the emergence of the ‘Industrial Data Scientist’: “These scientists will be a new breed of tech-driven, data-empowered domain experts with access to more industrial data than ever before, as well as the accessible AI/ML and analytics tools needed to translate that information into actionable intelligence across the enterprise.

“Industrial data scientists will represent a new kind of crossroads between our traditional understanding of citizen data scientists and industrial domain experts: workers who possess the domain expertise of the latter but are increasingly shifting over to the data realm occupied by the former.”

New tools

Many organisations are being impacted by a shortage of data scientists in proportion to demand, but Julien Alteirac, regional vice-president, UK&I at Snowflake, believes that new tools, powered by ML, could help to mitigate this skills gap in the near future.

“When it comes to analysing data, most organisations employ an abundance of data analysts and a limited number of data scientists, due in large part to the limited supply and high costs associated with data scientists,” said Alteirac.

“Since analysts lack the data science expertise required to build ML models, data scientists have become a potential bottleneck for broadening the use of ML. However, new and improved ML tools which are more user-friendly are helping organisations realise the power of data science.

“Data analysts are empowered with access to powerful models without needing to manually build them. Specifically, automated machine learning (AutoML) and AI services via APIs are removing the need to manually prepare data and then build and train models. AutoML tools and AI services lower the barrier to entry for ML, so almost anyone will now be able to access and use data science without requiring an academic background.”

The post Hot topics and emerging trends in data science appeared first on Artificial Intelligence.

For the restaurant industry, 2020 was a year marked by rapid innovation and adaptation amid the COVID-19 pandemic. Throughout the year, restrictions tightened and loosened across the country, forcing brands to adapt their business models at an unprecedented pace in order to survive. Now, in 2021, the industry’s focus is turned less toward adaptation and more toward improving operations and recapturing profits in the midst of supply issues, high third-party delivery fees, and staffing difficulties, which all strain the bottom line.

As a result, many chains have focused their energies on creating and maintaining online ordering platforms, introducing mobile applications, and incentivizing customers to return through customizable loyalty programs and targeted digital marketing campaigns. However, restaurants are now managing more channels than ever before, requiring a presence in drive thru, carry out, delivery, and online ordering, as well as in newly reopened dining rooms, making it even more challenging to manage these programs. While restaurants have turned to technology to streamline these initiatives, finding all the resources they need in one platform has traditionally been difficult, especially for small to mid-sized brands.

Just ask Justin Egan, chief marketing officer and co-founder at Wing It On!, a growing North Carolina-based quick-service chicken brand with eight brick-and-mortar locations across six states. Egan says that while the leaders of the brand—which first opened in 2011 and began franchising in 2014—knew its customer base was adopting online and mobile ordering, that technology was out of reach for smaller brands without deep enough pockets to develop it for themselves or to hire a vendor to build it. When they were finally able to introduce a mobile app in 2018, it was only able to list hours and locations and provide guests with photos of dishes.

“We decided we needed something more user friendly that could provide a branded Wing It On! experience and the functionality to not only manage digital orders, but that could also help us connect with our customers and drive digital revenue without requiring too much manual maintenance,” Egan says.

That’s when Wing It On! turned to Incentivio, an all-in-one digital platform that helps restaurants manage their marketing, branded mobile apps and web ordering, loyalty programs, delivery, gift cards, curbside pickup, and customer database. 

“Introducing online and mobile ordering that was fully integrated with our POS was really what drew us initially to Incentivio,” Egan says. “The platform is very robust and offers tons of great features for restaurants to leverage that would typically require integrating, paying for, and managing multiple platforms, databases, and vendors. We can manage our mobile and online ordering, loyalty programs, push notifications, rewards in one platform that also provides a machine learning driven marketing engine. It was a game changer for us at so many levels”

Rich Long, director of implementation at Incentivio, was previously an Incentivio customer in his role as director of IT for several different restaurants across the quick-service, fast-casual, and full-service segments. He says he knows firsthand just how cumbersome managing so many disparate systems can be.

“When you maintain multiple systems for your data, online and mobile ordering, loyalty programs, and more, your entire ecosystem is ripe for errors,” Long says. “This is often because if you change something in one place—say an item you offer in your online ordering system—you might forget to change it everywhere else. Having all these systems in one platform and a single database allows you to reduce errors and save both time and money, while also creating a single source of information so all your reports are integrated.” 

Egan says Incentivio makes managing the company’s loyalty program frictionless. Guests earn incentives based on their orders and actions, like referring friends, and each time the brand launches a new LTO, Incentivio makes it easy for Wing It On! to announce new offerings through both push notifications and email.  

“Incentivio has allowed us to create a digital experience that’s both convenient and easy for our fans and simple to manage for our company; our franchisees are often astonished at how much we can offer with this one system,” Egan says. “In fact, we’ve even seen candidates who passed on franchising with us in the past come back and sign franchise agreements with us because we use Incentivio.”

Not only has the system helped save Wing It On! time and generate sales through the built-in marketing tools, but the chain is now also using Incentivio’s new autonomous recommendation engine. This new suggestive upsell tool uses past data to understand which sides, desserts, and beverages are commonly ordered with center-of-the plate items and which items a guest already has in his or her cart to make smart recommendations that help restaurants generate incremental revenue and increase average check sizes. “Simply by turning this feature on, we’ve seen a 4 percent increase in digital revenue driven autonomously through the upsells engine—we didn’t have to lift a finger to start seeing results,” Egan says. 

“In 2020, the restaurant industry had to become lean,” Long says. “You can no longer have an IT department of 25 and an operations team three layers deep, so the more restaurants can simplify the better. By bringing all of these tools into one easy-to-use yet powerful platform, restaurants of all sizes have access to the technology they need to become more efficient, save time, and drive sales without a multi-million dollar IT budget.”

Long adds that just from the new upsell recommendations engine alone, some of Incentivio’s customers are generating more in automated incremental revenue each month that the cost of the entire Incentivio platform.  

“As an emerging brand that was actually thriving during the pandemic due to our digital presence, it was difficult to find technology partners that were also thriving and innovating,” Egan says. “We felt well positioned to have Incentivio as a partner during this time. They never dropped the ball, and that really stresses the importance of choosing the right vendor partners to grow with you through the thickest of times. Wing It On!’s future is bright, and we’re on trajectory to have 100 stores in the pipeline by 2024. We know Incentivio is able to quickly scale with us, and we’re excited to continue our relationship.”

The post A Machine Learning Powered Guest Experience Platform is Helping This Emerging Brand Grow appeared first on Artificial Intelligence.

AI jobs are Growing in Demand. Know which one suits you the best-

Artificial intelligence is taking over the tech space. AI-powered systems and applications are dominating every aspect of our lives. Tech leaders are constantly innovating and implementing AI technologies in business operations to drive growth and enhance customer satisfaction. Owing to the rising demand for artificial intelligence technologies, the tech industry is demanding skilled professionals; who can manage the new techs and help the companies process their operations efficiently. AI jobs have taken a turn and, skilled candidates are flocking towards the tech industry.

• HIRING ALERT! HERE ARE THE HOTTEST AI JOBS ACROSS THE GLOBE
• TOP AI JOBS ON LINKEDIN TO APPLY IN 2021
• VACANCY POST ALERT: TOP LUCRATIVE AI JOBS AVAILABLE IN MAY 2021

Here are the top 7 AI jobs for individuals interested in pursuing AI careers.

1. Intelligence Designer: Intelligence Designers are AI professionals who decide, manage, and estimate the strategic choices of how, when, and where to develop AI technologies based on complex and large IT systems. An intelligence designer needs to see the bigger picture and localize all the intelligence to understand and curate a strategy for the correct procedure to develop AI systems.

2. Data Evangelist: The role of data evangelism will become crucial for the adoption and growth of AI solutions. Companies soon will invest heavily in data evangelist roles to focus specifically on working across the organizations to educate users about the available AI solutions, the importance of data-driven decision-making, and the different ways to smoothly change the traditional business workflows to take advantage of the emerging technologies.

3. Machine Learning Engineer: The role of a machine learning engineer is currently highly sought after because of the rise in demand for machine learning technologies and programs. This role is compatible with candidates who have a degree in data science. The ML engineer will have to be adept in the various programming languages, as well as in AI programming.

4. Research Scientist: In the AI industry, the role of a research scientist is crucial. A research scientist needs to have a deeper understanding of the multiple disciplines of AI, like computational statistics, machine learning, deep learning, applied mathematics, and other related fields. To get hired for this job, one needs to have extensive knowledge about NLP, graphical models, and reinforcement learning.

5. AI Interaction Designer: An AI interaction designer is responsible for making sure that anything that the AI agents say is responsible, sensible, and coherent to the problem at hand. In the IT industry, there will be a growing need for professionals who can make AI systems usable for the mass audience.

6. Data Curator: A data curator uses self-service data platforms to curate data for different analytical tasks, allocates computational resources for accelerating data analysis, adds semantic data to the data catalog to speed up the analysis of high-value datasets, and blend data sets.

7. Business Intelligence Developer: The primary goal of a business intelligence developer is to analyze complex datasets and identify business and market trends to boost business revenues. The BI developer will use cloud-based data platforms to design, model, and maintain complex datasets. The candidate needs to have experience in data mining, data warehouse design, SQL integration services, SQL queries, and other related fields.

The post TOP 7 EMERGING AI JOBS FOR TECH PROFESSIONALS appeared first on Artificial Intelligence.

STUTTGART, Germany — NATO and its member nations have formally agreed upon how the alliance should target and coordinate investments in emerging and disruptive technology, or EDT, with plans to release artificial intelligence and data strategies by the summer of 2021.

In recent years the alliance has publicly declared its need to focus on so-called EDTs, and identified seven science and technology areas that are of direct interest. Now, the NATO enterprise and representatives of its 30 member states have endorsed a strategy that shows how the alliance can both foster these technologies — through stronger relationships with innovation hubs and specific funding mechanisms — and protect EDT investments from outside influence and export issues.

NATO will eventually develop individual strategies for each of the seven science and technology areas — artificial intelligence, data and computing, autonomy, quantum-enabled technologies, biotechnology, hypersonic technology, and space. But for the near future, the priority is AI and data, said David van Weel, NATO’s assistant secretary general for emerging security challenges.

The alliance expects to release an artificial intelligence strategy by this summer, he told Defense News in an exclusive March 4 interview. This effort comes on the heels of the U.S. Congress backing the creation of a national AI strategy in January as part of the country’s annual defense authorization bill.

NATO would do well to have its own AI and data policy strategies in place, van Weel said. He expects the strategies to include NATO’s plans for setting interoperability and technology standards across all EDT domains, and provide guidance on both principles for responsible use of AI-enabled platforms and export control mechanisms.

“It’s basically enabling the organization to then be able to start incorporating AI into military requirements for NATO itself, but also for our allies,” van Weel said. “Data and AI are the first [EDTs] that we will pick up with speed and we’ll deliver on this year.”

The EDT implementation strategy was endorsed during the alliance’s annual meeting of defense ministers in February, and followed the establishment of an EDT road map during the 2019 alliance summit in London. The defense leaders of NATO’s members along with their counterparts in Sweden, Finland and the European Union met virtually for the 2021 ministerial.

The overarching goal of the strategy was to create the conditions for continued interoperability across the alliance as it tackles “a whole new field” of technologies on the horizon. “One of the big challenges when we go into this new phase of disruptive technologies is how do you keep all allies on the same hymn sheet when it comes down to communicating with each other, using the same technology, being interoperable,” van Weel said. “So that’s a big part [of the strategy] and a big role for NATO to play.”

The post Artificial intelligence leads NATO’s new strategy for emerging and disruptive tech appeared first on Artificial Intelligence.

Lack of awareness about benefits of new courses and no information on available job opportunities are causes for less preference

VIJAYAWADA: Students and parents have preferred conventional engineering courses over emerging technologies. This is an aspect that has clearly emerged from the available trends of engineering seats being allotted in AP during the first phase of seat allotments.

The regular B. Tech CSE is in huge demand. New courses in emerging technologies like artificial intelligence, data science, machine learning, internet of things and cyber security have not found many takers. 94 per cent of CSE seats have been filled. Lack of awareness about the benefits of new courses and no information with regard to available job opportunities are causes for the lukewarm response, observers say.

Of the total 22,672 B. Tech CSE seats, 21,312 seats have already gone in the first phase of allotments itself, which reflects the demand for this course. There are 2,178 seats in computer science, artificial intelligence and machine learning. But only 1,346 of them have been filled.

Of the 1,885 seats in CSE data science, 1,243 have been filled. Likewise, of 1,533 seats in computer science artificial intelligence and data science 980 have been filled. Of 1,099 seats available in CSE artificial intelligence, 773 seats have been filled.

Similarly, CSE cyber security has 633 seats in which 401 have been filled. CSE internet of things has 497 seats. 331 of them have been filled. B. Tech artificial intelligence and machine learning have 276 seats. Only 181 have been filled. There are 184 seats in B. Tech internet of things (IoT) of which 85 have been filled. 137 seats are available in B. Tech artificial intelligence of which 76 have been filled.

With regard to B. Tech cyber security, 43 seats of total 115 seats have been filled. Only CSE IOT, blockchain technology with cyber security have got a good response, as 253 of total of 275 seats have been already been taken.

Educational experts feel parents and students are confused over the codes. For example, CSE Cyber Security is shown as CSEC, CSE Data Science as CSED and CSE Artificial Intelligence and Machine Learning as CSM.

Experts point out that engineering streams are changing. In the coming years, there will be a good demand for AI, data science, ITO, cyber security, machine learning and blockchain technology.

K. V. K. Rao, secretary, Federation of Self-Financing Technical Institutes, said the main cause is absence of knowledge among students and parents about emerging engineering streams. He said they had written a letter about changing of the codes into an easier form to highlight awareness about modern courses, which are future of the world.

Students S. Tharannum, P. Gayatri and others, who have opted for CSE, said they can study all subjects thoroughly in CSE. They can then opt for specialised courses in their higher semesters during the third year through Choice-Based Credit Systems (CBCS). They said they can take emerging technology courses under CBCS.The students also point out that presently, majority of corporate companies are choosing CSE students compared to students of emerging courses.

The post Lukewarm response to emerging engineering courses in AP appeared first on Artificial Intelligence.