Analytics Insight elaborates all essential information you need to know about Artificial Intelligence Engineering

Artificial Intelligence is the hottest disruptive technology in the tech-driven market around the world in the 21^st century.  It is making our lives more productive and smart by integrating machine learning algorithms into different products and services. The global market size of Artificial Intelligence is expected to reach US$93.53 billion in 2021. Multiple industries have realized that the combination of AI machines and human employees can boost productivity and gain massive revenue in the competitive market. Even the governments have started allocating millions of dollars into AI research and Development while the educational institutes are offering specialized degrees in Artificial Intelligence.

The most demanding job opportunity in this vast field is Artificial Intelligence Engineer. Yes, you must have known about the five traditional engineering courses in the education sector. But Artificial Intelligence Engineering is thriving in the market among tech-savvy students. They have realized that Artificial Intelligence is the future of the world and they can pursue AI Engineering to earn lucrative salaries from eminent organizations.

What is Artificial Intelligence Engineering?

Artificial Intelligence Engineering is one of the emergent engineering disciplines solely focused on creating and developing smart tools, machines, and systems to improve the standard of living of the society. AI Engineering covers a wide array of computing power and massive datasets with the integration of machine learning algorithms. This course helps businesses in smart decision-making processes to meet the needs of customers and enhance customer engagement. An engineering background is essential to create, manage and analyze AI functionalities efficiently. Artificial Intelligence Engineering provides a comprehensive framework and tools to design machine learning algorithms in a dynamic environment across the enterprise-to-edge spectrum. There are three pillars of Artificial Intelligence Engineering— Human-centric AI, Scalable AI, and Robust AI.

What are the roles and responsibilities of AI Engineers?

AI Engineers are required for developing, programming, and training the machine learning algorithms to make Artificial Intelligence models function like human brains and bodies. They do not need to write professional code with multiple programming languages but they have to locate enormous volumes of structured and unstructured real-time data from multiple sources. AI Engineering helps to create and manage the Artificial Intelligence development process and infrastructure of smart products and services. Explanable AI makes them explain about whole functionalities of AI models to partners, teams as well as stakeholders.

There is a high demand for AI Engineers from multiple industries and organizations such as retail, manufacturing, healthcare, finance, and many more. The average salary of an AI Engineer is around US$100,000 per year depending on the company. Numerous companies are recruiting AI Engineers with lucrative salaries such as Google, NVIDIA, Wipro, Concentrix, Jio, IBM, TCS, Cognizant, and many more.

Which skills are essential for qualifications?

• Bachelor’s or Master’s degree in Computer Science, Engineering, IT, and other relevant disciplines
• Programming skills in different languages like Python, Java, C++, and R
• Sufficient knowledge of linear algebra, probability, and statistics
• Basic experience in different tools such as Apache Spark, Hadoop, MongoDB, etc.
• Deep understanding of types of neural networks with frameworks related to it like PyTorch, TensorFlow, and many more
• Excellent problem-solving and communication skills

What are the top online certified courses for AI Engineers?

• Executive PG Programme in ML and AI from Udemy in association with IIIT Bangalore
• PG Programme in AI and Machine Learning from Simplilearn in partnership with Purdue and collaboration with IBM
• IBM AI Engineering Professional Certificate from Coursera
• Master’s in Artificial Intelligence from IntelliPaat

That being said, there are multiple educational institutes and professional websites that offer comprehensive details on Artificial Intelligence. This knowledge can drive aspiring AI Engineers towards success in professional career paths. One has to keep in mind that there is a plethora of opportunities in Artificial Intelligence Engineering. One has to keep an eye on the most suitable course or job opportunities according to the understanding and preference.

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At the time of Artificial Intelligence taking over the world rapidly, does engineering have any future?

Back then engineering was all about blueprints, sketches, and physical models. But today it is intensively about software tools and computer designs. The demand for artificial intelligence and digital technology has been gaining momentum. Advancements in the AI sector are transforming smart systems and supervised machine learning to a great extent.

Artificial intelligence systems will ease the laborious tasks that engineers do such as finding relevant content, fixing errors, and determining solutions. Smart systems can help finish their job quickly. AI and digital tech can also assist the system engineer in creating sophisticated designs along with incorporating sensor-based design procedures and delivering the designs to intelligent manufacturing facilities.

But AI may not approach the project the way a human designer would. Sometimes this can go off the grid too. Because today’s machines are usually made of expert systems that have software-enabled decision-making. Since the engine of this software is based on if-then rules, it is acquired through experience.

When new knowledge is registered in the library, the software uses if-then rules to expand new facts or ifs and comes out suggesting various solutions or then what occurs.

This process serves as the basis of AI and machine learning. As people are connected with the help of the internet, this opens the doors for smart machines to bring in new services and opportunities.

The largest fraction of smart systems is currently made of expert systems and this will be soon taken over by autonomous robots through technology transformation by 2024. There are critics as well as supporters for this trend. Since the number of robotic appliances continues to increase automatically, the cost of the sensors will decrease this becomes the simple reason for artificial intelligence robots to multiply within no time.

The robotic sensors market is estimated to undergo a CAGR of nearly 8% over the forecast period of (2021-2026). Currently, most of the industries like automotive, transportation, industrial manufacturing, logistics, and defense have started to adopt autonomous robotics and digital technology as their main mode of the production process.

As a result, due to this rapid growth of smart technologies with its roots, interconnected artificial intelligence can lead to uncertainty. Though computers can take intelligently-based action, they are not capable of replicating the cognition processes of the human brain. Algorithms of artificial intelligence tech can only deal with known data and cannot predict and formulate rational decisions during uncertain situations.

New technologies using the most advanced AI and machine learning have been coming up due to their widespread connectivity and inexpensive sensors. These technologies are primitive and still are not capable of mimicking the human brain.

It becomes clear that AI algorithms relate facts to solutions that are dependent on experiential learning without any acknowledgment of physics. AI has evolved from scientific advancement to an engineering tool. Latest innovations in digital tech require engineers from various domains to learn and integrate AI tools into their engineering designs.

Many open-source tools such as Microsoft’s DMLT, Google’s TensorFlow, and Amazon’s DSSTNE possess software libraries that empower machine learning. The DeepVariant open AI software of Google can depict a person’s genome from sequencing data more accurately than other methods are helping engineers to seek help from.

Personal productivity assistants like Amazon’s Alexa, Apple’s Siri, and Windows Cortana use natural language processing to make decisions. IBM Watson has been trained by Oncologists to help them treat and diagnose lung cancer. Tesla is getting closer to self-driving autonomous vehicles. Zebra Medical Systems, an Israeli company is developing tools for radiology having greater than human accuracy. All this is possible with the help of different types of engineers who are responsible for training smart systems.

At this point in time, the role of a human engineer may be that of a director shortly rather than producing and manufacturing the products. Though humans may not be executing the task, they are definitely the ones who are choosing the direction the machine should work. Once the machine knows how to design things, the system of engineering will change but engineers will still be highly skilled and relevant.

The uncertain future of technologies demands resilient and versatile engineers who can design robust technologies using artificial intelligence with different skill sets, including teaching AI systems how to innovate and become part of future human-AI organizations.

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Dr Sandip Bordoloi, Associate Professor, Department of Electrical Engineering, GIMT, Guwahati.

Electrical engineers work on a wide range of components, devices, machines, and systems, from tiny microchips to huge power station generators. Electrical systems, many a times, are so complex that they require expert systems to monitor and supervise them. Such supervisory systems may comprise high-end computers that use complex algorithms and software to regulate the flow of electrical energy obtained from different sources to domestic and industrial consumers.

A modern expert system such as Artificial Intelligence can pave the way to leverage data for ‘data interpretation’ and ‘decision-making’. The application of Artificial Intelligence technology on power systems is an active area of research and significant success has been achieved in this area to date. So, what is Artificial Intelligence and how can it be associated with Electrical Engineering? “Artificial Intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence”. Recent progress in areas like machine learning, big data analysis, and natural language processing has affected almost every area in academics, industry and business and, therefore, engineering is no exception. AI is the simulation of the process of data interaction of human thinking in a machine, so as to produce a smart machine, which can think like a human being to provide a problem, respond and deal with the problem. Machine learning and big data analysis can help leverage AI to build and optimise electrical systems by providing AI technology with new data inputs for interpretation and decision-making. In addition, harnessing AI’s potential may reveal chances of boosting system performance while addressing problems more efficiently.

AI is used to program machines to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. For years, researchers, engineers and scientists have been fascinated with the concept of how machines can be taught to replicate tasks ordinarily done by humans. Machines can now be fed with a huge amount of data to make them more ‘Artificially Intelligent’. The concept of AI is not new. Different forms of intelligent systems have been incorporated in Electrical Engineering for a long time, some of which are discussed below:

Expert systems: An expert system (ES) is a software system that captures human expertise for supporting decision-making. These systems are useful for online operations in the control field because they incorporate symbolic and rule-based knowledge, in the form of if-then rules to solve problems. The expert system consists of knowledge base, database, reasoning machine, interpretation mechanism, knowledge acquisition and user interface.

Fuzzy logic control systems: “Fuzzy logic is a basic control system that relies on the degrees of state of the input and the output depends on the state of the input and rate of change of this state”. The fuzzy control system uses a rule-based system for how machines will respond to inputs that account for a continuum of possible conditions, rather than straightforward binary rules.

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To be a modern-day engineer, being a domain-specific expert is just not enough. Data Science helps engineers to make better decisions at work and for organisations at large.

Why Should Companies Be Wary Of Shadow Data?

Every year, India produces over 1.5 million engineering graduates. These are disproportionately high numbers especially considering the appropriate opportunities available. To say that the competition in the job market is cutthroat, would be an understatement. In such a situation, it becomes imperative that students skill up and get that extra edge. 

Data science, apart from being a booming industry, is also a field that is interesting and challenging. Being trained in data science alongside regular engineering opens several doors of opportunity. Analytics India Magazine caught up with Dr Dakshinamurthy V Kolluru, Founder and President, INSOFE to understand more about the scope and opportunities. Dr Kolluru speaks about the engineering and data science dual degree offered by INSOFE which will equip aspirants with skills of today’s world.

AIM: How relevant are core engineers with data science specialisation in modern-day organisations?

Dr Kolluru: To be a modern-day engineer, being a domain-specific expert is just not enough. Organisations expect you to also have an understanding of data-driven innovation across various phases of engineering. Data Science helps engineers to make better decisions at work and for organisations at large.

An engineer’s role involves efficient design, development, execution, and maintenance of systems. This calls for an in-depth analysis of a variety of dynamic factors. Data Science, on the other hand, relies extensively on building a rationale around engineering endeavours. It involves collecting data on several factors affecting a product/problem, assessing the trends, analysing correlations between them, and predicting how they will change over time. 

Using the principles of data correlation and change predictions, engineers can come up with superior designs, build better products, and run systems more efficiently than ever before!  

AIM: Could you give us some examples of such applications in the real world?

Dr Kolluru: Automobile Industry

Industries such as Transportation and Automobiles depend on Artificial Intelligence and Data Science to build components with precision. Industry leaders such as GE Transportation have envisaged brilliant factories that rely extensively on Machine Intelligence. They have arrived at digitising the processes and reducing the downtime in maintenance. 

From Tool design standardisation, predicting wear and tear of moving parts to visualising exceptional malfunctions, Data Science is being adopted seamlessly. In addition, Artificial Intelligence is used for state-of-the-art innovations like aerodynamic design and autonomous navigation. 

With innovations and developments in data science applications engineers have been able to design superior solutions, which couldn’t be thought of earlier.

Healthcare providers are using statistical simulations of clinical trials to pre-test the efficacy of the drugs. Machine Learning algorithms are being utilised by the healthcare industry to identify different chemical combinations that can be used in drug discovery. Adopting machine intelligence to arrive at the most suitable combinations, companies are now able to come up with efficient drugs. These principles have sped up the drug development process and enhanced its efficiency.

Hospitals and doctors are now adopting machine learning models for prognosis, diagnosis, and treatment of chronic ailments like diabetes and terminal diseases like cancer.

AIM: How did INSOFE come up with the dual specialisation master’s programs?

Dr Kolluru: INSOFE is a world-class institute that is imparting quality data science education. To help students break into the heart of Engineering through their data science expertise, INSOFE has collaborated with top universities to offer dual specialization programs. 

Case Western Reserve University, USA and University of Strathclyde, UK are two such universities that have the finest engineering departments in the world. Case Western Reserve University in particular has been at the forefront of integrating engineering with various domains. Their work in Biomedical Engineering is an apt example of such innovation. 

The curriculum of the dual master’s degree is designed to ensure that students master data science and its implementation in different engineering domains. INSOFE’s philosophy is to extend the student’s expertise to research, product development, and new-age engineering in their core discipline. 

We strongly believe that such partnerships provide an opportunity for the student to study at some of the world-class universities, enhance student learning through multi-cultural interactions, and be mentored by renowned professors. 

AIM: What dual specialisations are offered through INSOFE and can students apply to such options in other universities?

Dr Kolluru: Case Western Reserve University and INSOFE offer Dual Specialization Masters programs in three engineering disciplines. The broad specializations include Biomedical Engineering with Translational Health Technology, Mechanical Engineering with AI and Robotics in addition to Civil, and Environmental Engineering with AI and Machine Learning. 

Students will complete the data science specific credits at INSOFE campuses in India and the latter part of the course work would be completed at Case School of Engineering, Cleveland – OH, USA.  

These programs are offered only under the Case Western – INSOFE collaboration. This option is not available for students undertaking normal courses at the university. The competitive examination scores like GRE / TOEFL / IELTS are waived off for students applying to the programs under this collaboration. However, the students have to clear INSOFE Entrance Exam (INEX) as a pre-qualification to apply for these programs. Eligible students also stand a chance to receive a scholarship of up to 25% of the tuition fee.  

AIM: How are opportunities different in the US compared to in India for students graduating in such programs?

Dr Kolluru: This is a STEM graduate program, students will be eligible to work in the USA upon the completion of the degree. The research and manufacturing focus of companies in the USA provides opportunities that require data science skills in addition to engineering expertise.

The dual specialization masters help students extend their reach beyond IT companies as they are equipped to work across industries like automobile, healthcare, petroleum, pharmaceuticals, and manufacturing. They have an added advantage over a regular engineering graduate since a greater number of organisations are looking for engineers with data science skills.  

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Data science interviews dropped by 15% in 2020 while data engineering interviews increased by 40%

Data science used to be the sexiest job of the 21st century. Now, the pandemic has cooled it down dramatically. The growth slowed down in 2020 after previously growing by 80% year over year in a new study by Interview Query. 

Interview Query analyzed over 10,000 related interview experiences from over 450+ companies to investigate how COVID-19 affected data science growth, tech company hiring, and interview processes.

The data shows that while the total number of data-related interviews is still growing, data science interviews dropped by 15% overall in 2020. However, FAANG companies (Facebook, Google, etc.) did not feel the economic squeeze and interviewed 25% more candidatesin 2020 versus 2019. This hiring was led by Amazon, which increased its interviews for roles by 40% over the last year.

Another notable shift has been the increase in data engineering and data analyst roles. Data engineering interviews increased by 40% in the past yearand business and data analyst interviews increased by 20%. Companies are likely cutting down on costs by hiring more data analysts rather than data scientists. Data science roles are also becoming more ambiguous and command a higher premium in salary versus data analyst and business analyst roles.

More interesting facts from the study:

• Take-home challenges were given in 25% of interviews.
• In FAANG interviews, take-home challenges were only given 8% of the time.
• The top interview questions asked in 2020 were on machine learning, coding and algorithms, and statistics.

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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.

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Microsoft Corp. today previewed a new Azure instance for training artificial intelligence models that targets the emerging class of advanced, ultra-large neural networks being pioneered by the likes of OpenAI.

The instance, called the ND A100 v4, is being touted by Microsoft as its most powerful AI-optimized virtual machine to date.

The ND A100 v4 aims to address an important new trend in AI development. Engineers usually develop a separate machine learning model for every use case they seek to automate, but recently, a shift has started toward building one big, multipurpose model and customizing it for multiple use cases. One notable example of such an AI is the OpenAI research group’s GPT-3 model, whose 175 billion learning parameters allow it to perform tasks as varied as searching the web and writing code.

Microsoft is one of OpenAI’s top corporate backers. The company has also adopted the multipurpose AI approach internally, disclosing in the instance announcement today that such large AI models are used to power features across Bing and Outlook.

The ND A100 v4 is aimed at helping other companies train their own supersized neural networks by providing eight of Nvidia Corp.’s latest A100 graphics processing units per instance. Customers can link multiple ND A100 v4 instances together to create an AI training cluster with up to “thousands” of GPUs.

Microsoft didn’t specify exactly how many GPUs are supported. But even at the low end of the possible range, assuming a cluster with a graphics card count in the low four figures, the performance is likely not far behind that of a small supercomputer. Earlier this year, Microsoft built an Azure cluster for OpenAI that qualified as one of the world’s top five supercomputers, and that cluster had 10,000 GPUs.

In the new ND A100 v4 instance, what facilitates the ability to cluster together GPUs is a dedicated 200-gigabit per second InfiniBand network link provisions to each chip. These connections allow the graphics cards to communicate with each across instances. The speed at which GPUs can share data is a big factor in how fast they can process that data, and Microsoft says its the ND A100 v4 VM offers 16 times more GPU-to-GPU bandwidth than any other major public cloud.

The InfiniBand connections are powered by networking gear supplied by Nvidia’s Mellanox unit. To support the eight onboard GPUs, the new instance also packs a central processing unit from Advanced Micro Devices Inc.’s second-generation Epyc series of server processors.

The end result is what the company describes as a big jump in AI training performance. “Most customers will see an immediate boost of 2x to 3x compute performance over the previous generation of systems based on Nvidia V100 GPUs with no engineering work,” Ian Finder, a senior program manager at Azure, wrote in a blog post. He added that some customers may see performance improve by up to 20 times in some cases.

Microsoft’s decision to use Nvidia chips and Mellanox gear to power the instance shows how chipmaker is already reaping dividends from its $6.9 billion acquisition of Mellanox, which closed this year. Microsoft’s own investments in AI and related develop have likewise helped it win customers. Today’s debut of the new AI instance was preceded by the Tuesday announcement that the U.S. Energy Department has partnered with the tech giant to develop AI disaster response tools on Azure.

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The Center for Noise and Vibration Control at KAIST announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis.

Professor Yong-Hwa Park from the Department of Mechanical Engineering developed a deep learning-based cough recognition model to classify a coughing sound in real time. The coughing event classification model is combined with a sound camera that visualizes their locations in public places. The research team said they achieved a best test accuracy of 87.4 %.

Professor Park said that it will be useful medical equipment during epidemics in public places such as schools, offices, and restaurants, and to constantly monitor patients’ conditions in a hospital room.

Fever and coughing are the most relevant respiratory disease symptoms, among which fever can be recognized remotely using thermal cameras. This new technology is expected to be very helpful for detecting epidemic transmissions in a non-contact way. The cough event classification model is combined with a sound camera that visualizes the cough event and indicates the location in the video image.

To develop a cough recognition model, a supervised learning was conducted with a convolutional neural network (CNN). The model performs binary classification with an input of a one-second sound profile feature, generating output to be either a cough event or something else.

In the training and evaluation, various datasets were collected from Audioset, DEMAND, ETSI, and TIMIT. Coughing and others sounds were extracted from Audioset, and the rest of the datasets were used as background noises for data augmentation so that this model could be generalized for various background noises in public places.

The dataset was augmented by mixing coughing sounds and other sounds from Audioset and background noises with the ratio of 0.15 to 0.75, then the overall volume was adjusted to 0.25 to 1.0 times to generalize the model for various distances.

The training and evaluation datasets were constructed by dividing the augmented dataset by 9:1, and the test dataset was recorded separately in a real office environment.

In the optimization procedure of the network model, training was conducted with various combinations of five acoustic features including spectrogram, Mel-scaled spectrogram and Mel-frequency cepstrum coefficients with seven optimizers. The performance of each combination was compared with the test dataset. The best test accuracy of 87.4% was achieved with Mel-scaled Spectrogram as the acoustic feature and ASGD as the optimizer.

The trained cough recognition model was combined with a sound camera. The sound camera is composed of a microphone array and a camera module. A beamforming process is applied to a collected set of acoustic data to find out the direction of incoming sound source. The integrated cough recognition model determines whether the sound is cough or not. If it is, the location of cough is visualized as a contour image with a ‘cough’ label at the location of the coughing sound source in a video image.

A pilot test of the cough recognition camera in an office environment shows that it successfully distinguishes cough events and other events even in a noisy environment. In addition, it can track the location of the person who coughed and count the number of coughs in real time. The performance will be improved further with additional training data obtained from other real environments such as hospitals and classrooms.

Professor Park said, “In a pandemic situation like we are experiencing with COVID-19, a cough detection camera can contribute to the prevention and early detection of epidemics in public places. Especially when applied to a hospital room, the patient’s condition can be tracked 24 hours a day and support more accurate diagnoses while reducing the effort of the medical staff.”

This study was conducted in collaboration with SM Instruments Inc.

/Public Release. The material in this public release comes from the originating organization and may be of a point-in-time nature, edited for clarity, style and length. View in full here.

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Automated machine learning (ML) sounds like the stuff of business leaders’ dreams.

Take the question, ‘which customers should we focus our marketing budget on this year?’ ML can now deliver robust answers to these types of business questions even faster than before, through greater use of automation.

Data in at one end, seriously useful business insight out of the other.

That is partly why Forbes predicts businesses will be investing $125bn a year on Artificial Intelligence and Machine Learning by 2025.

But even though numerous vendors boast of “AutoML” capabilities, the reality is that the act of developing ML models is still very much driven by humans and requires an awful lot of manual trial and error, performed by (expensive) experts.

Whilst the human element will never completely disappear, new automation techniques will help to reduce the vast amount of labor intensive work required. Not only will this reduce the overall cost and effort, it should reduce the levels of skill and experience required to build reliable ML models.

By today’s standards, it is certainly an unfortunate fact that manual effort still accounts for 80 percent of the machine learning development process. The most important part of this manual effort is the feature engineering process, where different data elements are combined and enriched to generate the most potent formula for predicting future events.

In the case of working out which customers might churn in the next year, for example, the data may include the size of their last discount. But prediction accuracy would improve if further features were engineered such as the time since the last discount, the average time between discounts and how the discount compares to those offered to other customers.

The challenge here is that nobody knows for sure whether these feature combinations will work until they have been developed, tested and fully assessed together as part of an ML model.

Specialist knowledge has been essential in these endeavors: you can’t produce a good algorithm without a subject matter expert knowing something about which features may be the most significant, or without experienced data scientists with deep knowledge of the ML process.

This need for expensive experts is one of the factors that have limited the application of ML to the organizations with the skills, patience and deep pockets to indulge lengthy developments, and to low-risk use cases with the clear potential for high levels of return on investment. But this is now starting to change.

One area of data science development that offers the potential to transform this endeavor is automated feature engineering: Using a computer to shortcut one of the most manually-intensive aspects of ML development.

The challenge of bringing automation to every stage of the ML workflow is one that my company, Peak Indicators, has been exploring for years. From this work, we created Tallinn ML, a platform providing all of the components required to build and deploy predictive models automatically, significantly reducing the reliance on highly-skilled data scientists.

Tallinn ML includes a unique feature-engineering engine that drastically cuts the time taken to develop new predictive algorithms by generating and testing thousands of different metrics as part of the data engineering, a process of trial and error that can take human months or even years to deliver.

Earlier this year we applied it on Kaggle – Google’s online home for the world’s data scientists and machine-learning experts, a kind of Premier League of ML. Kaggle set an unusual challenge. Can you develop an algorithm to predict which people were most likely to survive the world’s most infamous shipwreck – the Titanic?

Competitors were given a set of features, such as passenger age or gender, and asked to develop the most powerful algorithm to predict who would survive. Among Kaggle’s 1 million users are some of the world’s best-known researchers and data science teams. Peak’s Tallinn ML algorithm reached the top 5 percent for accuracy.

While other world-class competitors developed their models through manual means, our model was produced automatically. It involved no coding and no manual trial and error. It proved that machine learning has now reached a new level of automation.

• How automation can provide a foundation for digital transformation

The business impact of automated ML

So what difference does this make to business? Well, potentially a huge one.

The insights provided by predictive analytics and machine learning have been seen for some time as potentially revolutionary for business. Suddenly firms are far better able to answer crucial questions like:

• What are the impacts of a particular marketing campaign likely to be for specific target customers?
• Which of our employees are likely to leave in the next year?
• Which transactions in an account are most likely to be fraudulent?
• What seems to be causing a particular business problem?
• Harnessing the power of automation

Those questions are just the start. Answering them reliably means resources can be put where they are most needed. Inefficiently-used time and money can be reallocated to more productive tasks. Robust new insights into what is needed next appear magically.

But making that promise a reality is difficult. As Gartner highlighted just last year, “doing predictive analytics is tough. Your team needs to possess the right skills, understand business priorities and deal with data accuracy”.

That meant that any business, according to Gartner’s research, had previously to ask an important question: “What’s the likelihood you’ll sink under the weight of your organization’s data or swim to successful results?”.

Now that question is no longer so pressing. An automated solution makes it far more likely an organization will swim, because it will eliminate a considerable amount of time and effort in ML projects, and significantly reduce the need for very high-level expertise. The chances of an organization sinking – or treading water – in a sea of data become far smaller.

Problems that took months to solve previously can now be addressed in a matter of hours and days, and it has become economically viable to use ML to solve a much more extensive range of problems. We expect to see more experimentation and innovation using ML across all areas of business, including use-cases that didn’t justify the cost of data science projects lasting several months before.

Trials of Tallinn ML at a global retail and consumer-goods company produced a predictive model in two hours that was 18 percent more accurate, and delivered 19 times fewer false positives, than one developed over a three-month period by a team of experienced data scientists.

Another at a global financial-services organization showed that Tallinn ML’s automated feature engineering improved the accuracy of employee-churn predictions by 51 percent.

Beyond these improvements in pace and accuracy, this new approach promises to bring the benefits of ML to a much wider range of organizations. Automating the entire ML workflow democratizes data science to the point that any organization with an IT manager and big data sets to explore can start to derive value from it.

ML and the ability for algorithms to improve automatically through experience has long been recognized for its potential to bring greater intelligence and automation to the world of business. But to date, it has relied on expert humans to set up the machines to do what they do best.

Fully automating the development of ML models means that, for the first time, ML can deliver on its full promise. Efficiency. Productivity. Speed. Precision in prediction. Seriously useful business insight. Genuinely letting the machine take the strain, and freeing up humans to do what they do best.

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Where is the fun in a pinball machine that can play itself? How about pancakes that can cook themselves?

For students in Kennesaw State University’s Department of Mechatronic Engineering, the intrigue is less about the act and more about what goes on behind the scenes to make it possible. From a fully automated pinball machine to a pancake vending system that can cook without human intervention, several student teams recently took full advantage of their senior capstone coursework to prove that engineering can be creative while practical.

Since its inception, the mechatronics engineering department has emphasized building physical prototypes for its senior capstone coursework, allowing students to pitch projects of their own or select one from a pool of industry sponsors, said Kevin McFall, associate professor of mechatronics engineering and assistant dean for research in the Southern Polytechnic College of Engineering and Engineering Technology. However, what makes the mechatronics capstone process distinct from others across the college is that the students can select the minimum success criteria for their project. This means each team creates their own criteria for judgement, reports it to their professors and is solely judged on those self-made parameters.

In order to pass, McFall said students must demonstrate their ability to build something that achieves the three fundamental principles of mechatronics engineering: sense, think and act. All projects must have a mechanical design, a way to acquire data from a series of sensors, some sort of programmable device and a way to control an actual moving part.

Some students, like student Tyler Gragg, embrace the freedom to generate a unique project. A self-declared pinball machine aficionado, Gragg has always felt the desire to build a machine of his own. When he pitched the idea to teammates Kevin Kamperman, Cody Meier and Omar Salazar Lima, they conceived a design that would allow the pinball machine to play itself using a video camera to detect when the ball enters the “flipper zone,” which then triggers the flippers to move automatically and keep the ball in play.

Following the spread of the coronavirus, the team worked with staff members in the Department of Architecture to fabricate final pieces while they remained off campus. Since most teams completed most of their design work and built their components in advance, they were able to the see their projects to completion amidst the changes caused by COVID-19.

For Tim Ervin, inspiration for his senior capstone project came in the form of a YouTube video in which a team of engineers cooked a four-foot pancake using a robotic arm. Rather than make an impractical and gargantuan pancake, teammate Jay Strickland suggested that they make something that can cook several smaller pancakes. Along with fellow former students Brittney Smith and Ryan McHale, they built what they call a pancake vending machine, which can accept digital payment in exchange for one perfectly cooked pancake.

The self-contained machine is able to dispense the correct amount of batter onto a griddle, and then, with a spatula attached to a robotic arm, flip the pancake for an even cook on each side. The entire process can be done in just five minutes.

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