Change is the only constant thing, and failing to change with times is like walking on the path of self-destruction. Tech giants know this bitter truth. Hence, they are always following technological developments to make sure they adapt themselves quickly. The advent of data science and machine learning has brought profound changes globally, and tech giants have embraced it too. In recent years, tech companies are hiring data science experts and young professionals who have done ms in data science and masters In machine learning In India to ensure they have the workforce to understand this technology. They are also acquiring start-ups having data science expertise to stay updated with the developments in this field. Here, we take a look at how top tech companies are using data science and ML in their organization. 

• Chatbots:

An AI chatbot is a helpful tool that answers your messages for you. Facebook, Microsoft, and other organizations are pushing for the development of chatbots for use in their respective messenger apps.

Although chatbots didn’t start well and users bad-mouthed them for using profane language, chatbots can be useful in filtering spam and bigotry.

Tech giant Google has used AI to help its users in managing their messages. Google’s chatbot is a more sophisticated auto-response email and works on AI to send automated responses to messages. It understands what the email is asking and then frames an ideal response to it. 

• User Acquisition:

Generally, the customer acquisition funnel for a typical consumer business has three stages. The first stage involves segmenting the customer base to know their needs. In the second stage, the company engages with customers with the ideal messaging at the right time. And in the third stage, the business converts the customers into product users. 

Tech giants are using machine learning across the entire user acquisition funnel. In his 2017 letter to shareholders, Amazon’s CEO Jeff Bezos revealed how machine learning contributes to the Amazon.com experience ‘beneath the surface’ by enhancing product and deal recommendations based on user preferences. However, segmenting users and offering them relevant products is the first step and not the entire process. Several retailers bank on machine learning to adjust branding, copy, and promotional pricing on the go to maximize the likelihood of a sale for any potential customer

Salesforce launched Einstein, a product that scrutinizes CRM data to give tailored recommendations to increase a particular prospect’s chance to convert from a sales pitch. The product even suggests the right time to send an email.

• Voice computing:

Voice search is a crucial aspect of the digital era. It would make typing and many other tasks unnecessary and change how users search for something on the internet. 

Tech giant Microsoft is taking giant strides in this arena. Recently it acquired Maluuba, which according to Microsoft, has one of the world’s most impressive deep learning research labs for natural language understanding. 

By utilizing Maluuba’s research labs, Microsoft will hope it provides a sophisticated voice search option, a feature that major tech companies aspire to provide. 

• Automating learning and development:

The learning and development department is a crucial facet for any organization. And AI has already made stellar changes in the way organizations manage their L & D departments. AI has enabled HR teams to map and plan training requirements and automate the training process to a great extent. Some organizations have automated their entire training needs by using online Learning Management Systems (LMS). AI-powered online LMS systems transcend geographical limitations and time zones. These online LMS systems have minimized requirements for candidate and trainer travel. As this training model substantially reduces the HR functionaries’ workload and improves employee efficiency, AI-enabled training solutions are fast becoming a choice for companies globally. 

Forecasting:

Machine learning’s precision has allowed a wide variety of organizations to build more robust, granular, and accurate forecasting models.

Walmart was one of the pioneers in using AI in the field of supermarkets. It ran a competition in 2016 on the data science recruiting platform Kaggle, inviting applicants to use historical data from 45 stores to create a model that forecasted sales by the department for every store. Insurance giant AIG has been at the forefront in using data science. It has assembled a 125 person data science team to create machine learning models to improve the organization’s ability to anticipate claims and predict outcomes.

Global eyewear conglomerate Luxottica uses machine learning to forecast demand for its products. It uses machine learning and past launches’ data to predict sales performance.

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Medical equipment manufacturer Wipro GE Healthcare has partnered with the Indian Institute of Science (IISc) to open a research lab.

The lab is located at the Department of Computational and Data Sciences (CDS) in Bangalore. It will specialise in healthcare diagnostics, deep learning technology, ML and AI systems. Work will also be done on digital interfaces to produce sophisticated diagnostic and medical image reconstruction techniques.

This research unit will involve around fifty students and three faculty members of IISc to begin with. They will work closely with clinicians as well as Wipro GE Healthcare to integrate computational models into clinical workflows, to help doctors improve patient outcomes.

The partners are aiming for this collaboration between the worlds of industry and academia to solve some of the toughest challenges healthcare faces, using artificial intelligence and machine learning. One of their work streams will be exploring deep learning models to analyse lung lesions caused by COVID-19 via ultrasound and CT images.

Additionally they will apply deep learning models to improve opthalmology imaging, and medical image reconstruction methods.

The IISc was established in 1909 through a partnership between founder of the Tata group Jamsetji Nusserwanji Tata, the Maharaja of Mysore, and the Government of India. Over the 111 years since its establishment, IISc has become a major institution for advanced scientific and technological research and education in India.

Wipro GE Healthcare is supporting the research lab with a grant as part of its CSR efforts. This funding will be used to equip the lab with the necessary hardware and software. This includes state-of-the-art deep learning servers and an advanced visualization platform.

Commenting on the collaboration, Dileep Mangsuli, Chief Technology Officer at GE Healthcare South Asia said: “The world’s healthcare is transforming through use of digital technologies which can enable precision health. This transformation can be accelerated by building a collaborative ecosystem of industry and academia partners.

“This Healthcare Innovation Lab at IISc will help bring to market unique digital solutions which will get integrated into our Edison platform and intelligent devices, helping clinicians solve some of the toughest healthcare challenges.”

Prof. Phaneendra Yalavarthy, convener of the lab as well as the Chair of the Office of Development and Alumni Affairs at IISc, said: “Private-Public partnership is in the DNA of IISc and this collaborative lab in the space of artificial intelligence in healthcare funded by Wipro GE Healthcare is timely, given the push for digital technologies.

“Translation of the research work carried out in the lab into the clinic will be the priority, and there is no better industry partner in India than Wipro GE Healthcare that can accelerate this. This is only the beginning of the collaboration and we are hoping to scale up the research activities in the near future.”

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UNIVERSITY PARK, Pa. — Registration is now open for Penn State’s Institute of Computational and Data Sciences’ (ICDS) 2020 Symposium. The two-day symposium will be held virtually Oct. 21-22 and will feature an interdisciplinary group of speakers and experts who will focus on both the challenges — and opportunities — of big data and data science.

Because the symposium was rescheduled due to the pandemic, those who registered for the spring 2020 event will need to re-register to attend the upcoming event.

The symposium will feature two keynote presentations and four expert-led panel discussions. The two keynote presentations include: “ZettaScale Computing on Exascale Platforms,” presented by Shantenu Jha, chair of Computation and Data Driven Discovery (C3D) Department at Brookhaven National Laboratory and associate professor of computer engineering at Rutgers University, and “The Landscape of Data Science: Basic Research to Impact,” presented by Chaitan Baru, senior science adviser, Convergence Accelerator, Office of the Director, National Science Foundation.

Jha has collaborated with scientists from multiple domains, including molecular and earth sciences and high-energy physics. His research interests are at the intersection of high-performance and distributed computing, computational science and cyberinfrastructure.

Baru has led or has co-led a number of data cyberinfrastructure initiatives, including work as the principal investigator (PI) of the OpenTopography project; cyberinfrastructure lead, Tropical Ecology, Assessment and Monitoring network; and a co-investigator of the Cyberinfrastructure for Comparative Effectiveness Research project.

Topic for the four panel discussions include:

• “Big Data, Agriculture and Food Supply,” organized by Asad Azemi, associate professor of engineering, Penn State Brandywine
• “Artificial Intelligence and Machine Learning in Manufacturing,” organized by Soundar Kumara, Allen E. Pearce and Allen M. Pearce Professor of Industrial Engineering
• “Social Engineering with Data: Disinformation and Destabilization of Geo-Political Order,” organized by Anne Toomey McKenna, Distinguished Scholar of Cyber Law & Policy, Penn State Dickinson Law, and co-hire, Institute for Computational and Data Sciences
• “Data & Genetics/DNA: Value, Ethics and Risks,” organized and moderated by Aleksandra (Sesa) Slavkovic, professor and associate dean for graduate education, Eberly College of Science

Faculty, students, staff and industry and funding agency representatives are welcome to attend. The full agenda is available online.

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It’s no secret that in most companies HR is under pressure to reduce costs. As the international mobile workforce becomes increasingly more diverse, so do its policies to meet various business needs.As a result, companies with a global mobility agenda are operating in an ever more complex and fragmented environment. But could data be the answer?At its best, data analytics and metrics can measure, streamline and centralise the administration of international assignments. More importantly, the potential to save time could significantly enhance the performance and impact of global mobility and talent teams to play a more strategic role within their companies.

A wider role for global mobility

Steve Black, co-founder of MOVE Guides, headquartered in London – who help HR teams across multinational firms move their employees around the world, notes there is a crucial role for data to play in global mobility.“10 years down the line, not only for HR but also for mobility specifically, it will be about using data to push the strategic agenda and be part of the conversation in the planning stages. Rather than waiting for the plan and figuring out how to use mobility to execute against that.”But data still remains an elusive beast for many in the global mobility sector. According to a Mercer Insight 2015 report, a staggering 90 per cent of European companies do not use metrics to track assignment success and results, and only 21 per cent use specialist software to consolidate assignment data. The root of the problem? Lack of data confidence and an understanding of what data analytics can do.In his book, Confident Data Skills, author Kirill Eremenko, explains the value of data science and an analytical mindset. He notes that while many company divisions will already be familiar with business intelligence, it only has the limited ability to describe what has happened. Data science on the other hand, has the power to predict and analyse.

“An ability to use data science tools eliminates the human burden of looking for insights manually, enabling you to focus on isolating bottlenecks, uncovering sales opportunities and evaluating the health of a business division.”However, Eremenko notes that many corporate professionals across multiple sectors, particularly HR and global mobility teams could have an ill-perceived concept of data due to a reliance on spreadsheet programmes such as Microsoft Excel and similar tools. “Excel can have the effect of over simplifying things and so people have a skewed perception of data. If the only data you know is Excel, you have to be open to changing your perception of analytics.”The reason being that in any database management system worth its salt, data and logic must be considered separately. Instead, Eremenko recommends professionals use Python and RStudio to analyse datasets in their sector.According to technology research firm Gartner, business data analytics can be ultimately divided into four key segments: What happened? Why did it happen? What will happen? And, more crucially, what do we do now?For mobility professionals, the combination of descriptive and predictive analytics could help them position their teams more effectively as a strategic asset. From knowing how many assignees are located across the globe to managing absence, employee retention rates and using real-time statistics to assess future assignment requirements.Creating a collective dashboard where all parties can access assignment information and a cost portfolio allows mobility teams to quickly access assignee profiles, helping to get the right talent to the right location at the right time. It also allows teams to track and ensure compliance issues are being dealt with effectively.For larger relocation programmes, data analytics can be invaluable for employers. At the end of an assignment, core data can be used to dissect the performance of assignees in one country over another and root out negative impacts to employee performance. Behavioural analysis on what motivates an employee can also precisely inform talent strategies across a particular demographic. To marry the best type of compensation and encourage the right kind of employee behaviour, for the best possible business outcome.

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The concept of robotics has been in existence for a long time, with Egyptians using automated water clocks to strike the hour bell and hydraulically operated statues that could gesture and speak in 400 BC. Subsequently, there have been many such instances of robotics in the history of mankind. The first modern-day Industrial Revolution dates back to 1800s and had manufacturing processes for metals, chemicals, textiles and mining; leading to an increase in productivity and output. Robots have evolved tremendously over the years and are now being widely used in various sectors such as defence, disaster management, search and rescue operations, and the entertainment industry in the form of electronically operated toys.

Automation is an extension of robotics and can be termed as the next phase of industrial revolution. The present industrial revolution seeks to disrupt the existing processes and enhance them with programmable logic. While it is easy to identify a repetitive process or task, it is equally difficult to program such a code that can make a machine carry out this activity on a perpetual basis.

As technology has improved over time, robots and automated systems have made inroads into organizations where tasks may have been dangerous, impossible or just plain mundane for humans.

Since the dawn of computer programming, automation, also known as robotics, was available in the form of click-and-type macros. These would repeat keyboard and mouse operations, mimicking a human.

With the advent of advanced analytics and data sciences, as in artificial intelligence, it is now possible to automate complex tasks that can act intelligently like humans. Analytics are now being used to identify or avoid risks; for example, identifying a suspect fraudulent transaction on a credit card based on the customer’s regular spending pattern or studying a customer’s spending pattern on an online retail store and recommending products.

Use of sensors in everyday objects such as lights, air conditioners and televisions—which operate based on inputs like human gesture, speech or commands—is another example. Sensors are also being used to identify speeding cars or count the number of parking slots available in large parking spaces.

The latest application of robotics and automation can be seen in technologies such as autonomous or driverless cars, 3D printing and chat bots.

Data analytics forms the backbone of robotics and automation. Any task that can be programmed into a computer-readable code requires extensive amount of input data to be analysed and processed in real-time basis to provide enhancements.

For instance, real-time data analytics plays a pivotal role in allowing a driverless vehicle to self-navigate from one point to another, without human intervention. Sensors and cameras provide real-time input of distance between vehicles, traffic conditions, and natural obstacles such as stones and dividers; which are then processed at high speeds to allow the vehicle to navigate at an optimum speed. Global Positioning System (GPS) provides navigation and route information for the destination. All these processes and sensors work simultaneously, processing large data sets to redefine the driving experience.

Chat bots too require complex understanding to simulate human behaviour for efficient customer service. Data analytics can provide significant value to chat bot technology by leveraging large data sets that form the basics to simulate human behaviour. With the help of artificial intelligence and machine learning, bots can be designed to continuously learn and evolve their responses to customer queries. Chat bots can also be used in help desk management systems where these are capable of resolving queries accurately and at a faster pace compared to their human counterparts.

While automation technologies like driverless cars and chat bots may disrupt our lives in the future, each one of these could potentially create avenues and opportunities for individuals and businesses. Here are some examples:

• The mass adoption of driverless cars could potentially have an adverse short-term impact in the form of job losses, but may also allow low-cost entry for small scale investors. These investors can set up a unit of driverless cabs and earn their livelihood without relying on third parties. Programming and data analytics for driverless cars would result in job creation in software engineering.

• Chat bots could possibly reduce the need for customer service representatives but on the other hand, complex programming requirements and artificial intelligence would lead to more job creation for data science analytics and service delivery to customers.

Every industrial revolution that has occurred in the past has opened a wide variety of prospects for individuals as well as organizations.

Market sentiments suggest that the job market does not stay static but changes constantly with innovation in technologies. Many tasks undertaken (manually) by humans about 20-30 years ago are no longer relevant. Gone are the days wherein one would need to feed a huge stack of chip cards to a large computer system. Data entry has become more sophisticated and less manual. Similarly, today’s jobs may not be that relevant 20-30 years in the future but there would be more and different opportunities. With the increased use of remote connectivity, video conference and digital presence; the job scenario is expected to drive the future of work. Manual tasks would become increasingly automated for business efficiencies and scale. This will be key for organizations that want to stay ahead of the curve and outpace rivals in a highly competitive world.

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