AI has finally come full circle.

A new suite of algorithms by Google Brain can now design computer chips—those specifically tailored for running AI software—that vastly outperform those designed by human experts. And the system works in just a few hours, dramatically slashing the weeks- or months-long process that normally gums up digital innovation.

At the heart of these robotic chip designers is a type of machine learning called deep reinforcement learning. This family of algorithms, loosely based on the human brain’s workings, has triumphed over its biological neural inspirations in games such as Chess, Go, and nearly the entire Atari catalog.

But game play was just these AI agents’ kindergarten training. More recently, they’ve grown to tackle new drugs for Covid-19, solve one of biology’s grandest challenges, and reveal secrets of the human brain.

In the new study, by crafting the hardware that allows it to run more efficiently, deep reinforcement learning is flexing its muscles in the real world once again. The team cleverly adopted elements of game play into the chip design challenge, resulting in conceptions that were utterly “strange and alien” to human designers, but nevertheless worked beautifully.

It’s not just theory. A number of the AI’s chip design elements were incorporated into Google’s tensor processing unit (TPU), the company’s AI accelerator chip, which was designed to help AI algorithms run more quickly and efficiently.

“That was our vision with this work,” said study author Anna Goldie. “Now that machine learning has become so capable, that’s all thanks to advancements in hardware and systems, can we use AI to design better systems to run the AI algorithms of the future?”

The Science and Art of Chip Design

I don’t generally think about the microchips in my phone, laptop, and a gazillion other devices spread across my home. But they’re the bedrock—the hardware “brain”—that controls these beloved devices.

Often no larger than a fingernail, microchips are exquisite feats of engineering that pack tens of millions of components to optimize computations. In everyday terms, a badly-designed chip means slow loading times and the spinning wheel of death—something no one wants.

The crux of chip design is a process called “floorplanning,” said Dr. Andrew Kahng, at the University of California, San Diego, who was not involved in this study. Similar to arranging your furniture after moving into a new space, chip floorplanning involves shifting the location of different memory and logic components on a chip so as to optimize processing speed and power efficiency.

It’s a horribly difficult task. Each chip contains millions of logic gates, which are used for computation. Scattered alongside these are thousands of memory blocks, called macro blocks, which save data. These two main components are then interlinked through tens of miles of wiring so the chip performs as optimally as possible—in terms of speed, heat generation, and energy consumption.

“Given this staggering complexity, the chip-design process itself is another miracle—in which the efforts of engineers, aided by specialized software tools, keep the complexity in check,” explained Kahng. Often, floorplanning takes weeks or even months of painstaking trial and error by human experts.

Yet even with six decades of study, the process is still a mixture of science and art. “So far, the floorplanning task, in particular, has defied all attempts at automation,” said Kahng. One estimate shows that the number of different configurations for just the placement of “memory” macro blocks is about 10^2,500—magnitudes larger than the number of stars in the universe.

Game Play to the Rescue

Given this complexity, it seems crazy to try automating the process. But Google Brain did just that, with a clever twist.

If you think of macro blocks and other components as chess pieces, then chip design becomes a sort of game, similar to those previously mastered by deep reinforcement learning. The agent’s task is to sequentially place macro blocks, one by one, onto a chip in an optimized manner to win the game. Of course, any naïve AI agent would struggle. As background learning, the team trained their agent with over 10,000 chip floorplans. With that library of knowledge, the agent could then explore various alternatives.

During the design, it worked with a type of “trial-and-error” process that’s similar to how we learn. At any stage of developing the floorplan, the AI agent assesses how it’s doing using a learned strategy, and decides on the most optimal way to move forward—that is, where to place the next component.

“It starts out with a blank canvas, and places each component of the chip, one at a time, onto the canvas. At the very end it gets a score—a reward—based on how well it did,” explained Goldie. The feedback is then used to update the entire artificial neural network, which forms the basis of the AI agent, and get it ready for another go-around.

The score is carefully crafted to follow the constraints of chip design, which aren’t always the same. Each chip is its own game. Some, for example, if deployed in a data center, will need to optimize power consumption. But a chip for self-driving cars should care more about latency so it can rapidly detect any potential dangers.

The Bio-Chip

Using this approach, the team didn’t just find a single chip design solution. Their AI agent was able to adapt and generalize, needing just six extra hours of computation to identify optimized solutions for any specific needs.

“Making our algorithm generalize across these different contexts was a much bigger challenge than just having an algorithm that would work for one specific chip,” said Goldie.

It’s a sort of “one-shot” mode of learning, said Kahng, in that it can produce floorplans “superior to those developed by human experts for existing chips.” A main throughline seemed to be that the AI agent laid down macro blocks in decreasing order of size. But what stood out was just how alien the designs were. The placements were “rounded and organic,” a massive departure from conventional chip designs with angular edges and sharp corners.

Human designers thought “there was no way that this is going to be high quality. They almost didn’t want to evaluate them,” said Goldie.

But the team pushed the project from theory to practice. In January, Google integrated some AI-designed elements into their next-generation AI processors. While specifics are being kept under wraps, the solutions were intriguing enough for millions of copies to be physically manufactured.

The team plans to release its code for the broader community to further optimize—and understand—the machine’s brain for chip design. What seems like magic today could provide insights into even better floorplan designs, extending the gradually-slowing (or dying) Moore’s Law to further bolster our computational hardware. Even tiny improvements in speed or power consumption in computing could make a massive difference.

“We can…expect the semiconductor industry to redouble its interest in replicating the authors’ work, and to pursue a host of similar applications throughout the chip-design process,” said Kahng.

“The level of the impact that [a new generation of chips] can have on the carbon footprint of machine learning, given it’s deployed in all sorts of different data centers, is really valuable. Even one day earlier, it makes a big difference,” said Goldie.

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Analytics Insight explains the difference between big data engineers vs Artificial intelligence engineers.

‘A domain for the nerds,’ this is what technology was called in the late 1900s. However, a lot of things changed in the 21st century. In the digital world, we welcome hundreds of new Artificial intelligence-powered tools and solutions every day. Owing to the drastic surge in the implementation of artificial intelligence, the technology market has opened its door to more jobs. On the other hand, big data is also bringing many organizational changes to companies. Big data was previously seen as useless content occupying most of the memory in data centers. Fortunately, when technology evolved and became advanced, businesses realized the importance of big data and used it to get data-driven decisions. Following the upsurge in big data and artificial intelligence, two profiles namely big data engineer and artificial intelligence engineer took center stage. According to LinkedIn’s 2020 Emerging Jobs report, artificial intelligence engineers and data-related jobs continue to make a strong showing as the top emerging job roles for 2020 with 74% annual growth in the past four years. Big Data Engineering vs Artificial Intelligence Engineering is two data job roles that are often used interchangeably due to their overlapping skillset but are actually different. In this article, Analytics Insight explains the difference between big data engineers vs AI engineers and helps you choose the right career.

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• 5 HABITS OF A SUCCESSFUL AI ENGINEER. DO YOU HAVE THESE?

Definition 

Big data engineer: Big data engineering is a branch of data science that deals with the practical applications of data analysis and collection. A big data engineer is in charge of the design and development of data pipelines. They intensely work to collect data from various sources and give it for further processing to analysts and data scientists. Even though the profile is not directly connected to business teams and business decision-making, it centers on developing systems for better flow and access to information.

Artificial intelligence engineer: An artificial intelligence engineer is someone who works with algorithms, neural networks, and other tools to advance the field of artificial intelligence. They deal with artificial intelligence problems and solve them. Artificial intelligence engineers develop techniques and use them in commerce, science, and other fields. They must be able to extract data efficiently from a variety of sources, design algorithms, build and test machine learning models, then deploy those models to create AI-powered applications capable of performing complex tasks.

Roles and responsibilities

Big data engineer: A big data engineer has to design, develop, construct, install, test, and maintain the complete data management and processing system. Their key role is to seek the raw data and make it usable for other professionals. Without a big data engineer, the company can’t collect data from various sources. Not just collection, they also engage in managing the collection of data and handles its storage, and process it for further use. Some of the other routine responsibilities of a big data engineer are as follows,

• Build highly scalable, robust, and fault-tolerant systems to manage high volumes of data.
• To introduce new big data management tools and technologies to stay ahead in the race.
• Explore various choices of data acquisitions and try out new ways to use existing data.
• Create a complete solution by integrating a variety of programming languages and tools together.
• Employ disaster recovery techniques in case of mishaps.

Besides the basic responsibilities, big data engineers are expected to be well-versed in a set of technological aspects. They should have in-depth knowledge of big data technology and communicate the ideas within and out of the team. In order to carry out the task, they should be experts in the following context.

• Basic knowledge about Java, data structuring, and big data.
• Familiarity with NoSQL solutions, Cassandra, HIVE, CouchDB, and HBase.
• Experience in analytics, OLAP technologies, and more.

Artificial intelligence engineer: Besides creating techniques, artificial intelligence engineers are assigned other organizational responsibilities as well. In order to integrate their technique across the enterprise, artificial intelligence engineers must be able to overcome the unique challenges that result from combining the logic of traditional business applications with the learned logic of machine learning models. Some of the other responsibilities are as follows,

• Build artificial intelligence and machine learning models, then convert the machine learning models into application program interfaces (APIs) so that other applications can use them.
• Help stakeholders understand the output yielding.
• Set up and manage AI product infrastructure and the automation of the infrastructure used by an organization’s data science team.
• Conduct statistical analysis and interpret the results to help organizations drive data decisions.

So, what should you choose as your career option?

According to the World Economic Forum, artificial intelligence was anticipated to create over 58 million jobs by the end of 2020. As we are already in the middle of 2021, artificial intelligence engineering and big data engineering are seeing a sweeping demand rise in the job market. But while choosing a career between these two, you should validate your interest and preferences. If you are someone who is solely interested in data and big data management, it is safe to say that you are destined to work as a big data engineer. If you like coordinating with other teams and want to work out of the clustered data, then artificial intelligence engineering will better suit you.

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Artificial intelligence is a disruptive technology that is enabling technological revolutions in its initial stages. In simpler terms, AI is any behavior or activity portrayed by machines and systems. From a computing perspective, artificial intelligence imitates human behavior by analyzing past data. AI has self-learning attributes which means whenever an AI-powered machine receives new information, it can make corrections on its own to make sure it doesn’t repeat old errors. This property allows AI systems to perform tasks that require human intelligence and human understanding like visual perception, speech recognition, decision making, etc.

According to research done by Gartner, artificial intelligence will create a business value of $3.9 trillion by 2022. It also suggests that artificial intelligence will continue to be the most disruptive technology for another decade, thanks to its use cases in leveraging computer power, capacity, speed, and data diversity. This growth results in an explosive amount of demand for the right talent in this field and its many disciplines, including artificial intelligence engineering.

What Is Artificial Intelligence?

In theory, artificial intelligence engineering is the usage of algorithms, computer programming, neural network, and other technologies to develop AI techniques and applications. These techniques have use cases in commerce, science, and other domains. Therefore, an artificial intelligence engineer should be capable of extracting data efficiently from a variety of sources, algorithms, and machine learning models to create AI-backed applications capable of performing complex tasks.

Can You Be An AI Engineer?

Like any profession, AI engineers need to cover some basic requirements. Obviously, a bachelor’s degree is an essential first step to become a successful AI engineer. Having a good grasp on foundation subjects like computer science, mathematics, information technology, statistics, finance, and economics. After covering the basics, aspiring AI engineers must focus on specialized subjects like data science, machine learning, and NLP (natural language processing). These subjects are usually available as certificate programs at universities, coding schools, and other institutions. Having industry certifications for machine learning, deep learning, computer programming languages like Python, Django, JavaScript, and data science is a bonus.

Apart from these certifications and degrees, these employers look for the following technical skills:

Technical Skills

To work on algorithms, machine learning, neural networks, and other AI-backed technologies, an AI engineer must be good at programming and have a good understanding of the software development lifecycle, coding techniques, and new techniques.

Programming languages such as Python, R, Java, and C++ are important for the construction and implementation of AI models. Having a thorough knowledge of at least one of the major programming languages and tools is a bonus.

Mathematical Skills like linear algebra, probability, and statistics help AI engineers to understand the functioning of various AI models like Hidden Markov, Naive Bayes, Gaussian mixture models, etc.

Business Skills

In a business setting, artificial intelligence engineers need to understand how a machine learning system can adapt to the changing dynamics of the business. An AI engineer must be quick to decide when a machine learning model is ready for business use and monitor its performance accurately to judge if there needs to be any alterations. Like any other profession, having basic knowledge beyond the domain will help in scaling up the career. For example, knowing how the core business runs, who the target audience is, what is the market condition like, etc. will help an AI engineer put his knowledge to the best use.

Soft Skills

Communication and the ability to collaborate with a team are something that hiring managers look closely for. The ability to think creatively, practically, and objectively will help an AI engineer to solve business problems quickly. And what’s the point of the AI engineer not being unable to communicate his/her ideas to the rest of the organization and stakeholders? Therefore, having communication and presentation skills is a must.

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Cloud Ace, Datatonic, Deloitte Consulting, Devoteam, Pandera Systems, Pythian Services and Quantiphi are among the 50-plus Google Cloud partners with employees who’ve already earned the cloud provider’s new Professional Machine Learning Engineer certification.

The certification, unveiled last week, validates cloud professionals’ expertise in designing, building and productionizing machine-learning (ML) models to solve business challenges using Google Cloud technologies, along with their knowledge of proven ML models and techniques.

Finding employees with the right ML skills has been among the top challenges for IT leaders this year, Google Cloud said.

The pre-pandemic tech talent shortage challenged many organizations’ digital transformations, and they’re now playing catch-up, according to a September report from staffing company Robert Half International that highlights IT industry trends and starting salaries that its recruiters expect to see next year.

“They are faced with the need to accelerate their transformation process as well as address technical debt within their organization,” the report said. “Many are seeking technology professionals with expertise in AI and machine learning, cloud computing and robotic process automation.”

Artificial intelligence (AI)/ML specialists are expected to have among the highest median starting salaries for non-executive IT jobs in the U.S. next year, according to the report.

When Ottawa’s Pythian Services heard earlier this year there potentially would be a new ML-related certification coming from Google Cloud, it put its ML team on notice and had them stand by to get it done, according to Vanessa Simmons, vice president of business development for the cloud, data and analytics services company, a Google Cloud Premier Partner.

Pythian currently has two Google Cloud-certified Professional Machine Learning Engineers, with the goal of having its entire team certified. Carlos Timoteo, a Pythian data scientist and machine- learning engineer, has been working with Google Cloud, applying AI Platform, BigQuery and big data services to implement ML solutions for the company’s customers.

Timoteo took his first Google Cloud certification exam, the Google Cloud Professional Data Engineer, in 2017. Since then, he and his work colleagues and fellow data scientists in his circle have been waiting for a well-designed data scientist/ML certification, he said.

“The preparation was not too hard or long given my experience as a data scientist leveraging Google Cloud,” Timoteo said. “I used the provided preparation guide to identify the points I needed to study more to ace the exam, leveraging Google Cloud documentation, the Google Developer Machine Learning Crash Course and a couple books.”

The Google Cloud Professional Machine Learning Engineer certification exam has a big emphasis on engineering ML solutions, according to Timoteo. The data science portion of the exam is more focused on the technique than on the algorithm details, implementation and limitation, he said. Beginners should expect snippets of code in Python and SQL and should learn TensorFlow 2.x and its ecosystem and how to implement TensorFlow 2 on Google Cloud Platform (GCP) in production, he noted.

“The exam is a valuable tool in assessing if the exam taker is able to propose a solution that satisfies many requirements recurrent for a variety of solutions, in several industry verticals,” Timoteo said. “With this added experience, Google and our customers can trust in my team and myself to build the most elegant and suitable solution to satisfy their business demands leveraging Google products and best practices in the market.”

London-based Datatonic, Google Cloud’s 2019 Specialization Partner of the Year for AI and ML, has two employees who earned the new certification.

“The Google Cloud Professional Machine Learning Engineer certificate gives a valuable overview of production ML on Google Cloud Platform, particularly on designing solutions with ML best practices in mind, such as mitigating model bias and utilizing GCP tools to interpret model predictions,” said Julian West, a Datatonic data scientist. “This will be actionable for future projects with clients increasingly conscious of model bias and explainability in their ML projects.”

Three Deloitte Consulting Google Cloud practitioners have earned the new certification.

“Deloitte teamed with Google early on with unwavering commitment to the certification program given their market leadership in AI/ML,” said Tom Galizia, lead commercial partner for Deloitte Consulting‘s Alphabet/Google alliance. “[Google CEO Sundar Pichai] has stated Alphabet overall is an AI-first company, which is clearly reinforced with their broad and deep portfolio of AI/ML/analytics-based technologies at unprecedented cost curves and commitment to the democratization of AI/ML.”

The Google Cloud Professional Machine Learning Engineer certification requires a two-hour exam. The cloud provider recommends candidates have at least three years of industry experience, including one or more years designing and managing solutions using GCP. An ML engineer is proficient in model architecture, data pipeline interaction and metrics interpretation, and requires familiarity with application development, infrastructure management, data engineering and security, according to Google Cloud.

The certification exam evaluates candidates’ abilities to frame ML problems, develop ML models and architect ML solutions. It also assesses their abilities to automate and orchestrate ML pipelines, prepare and process data, and monitor, optimize and maintain ML solutions.

Google Cloud partners also can earn Expertises in AI and ML, including in Google Cloud AI and ML APIs, Contact Center AI, Document AI and Visual Intelligence. Quantiphi, SoftServe and SpringML are among the more than 90 partners with those Expertises.

Partners who earn a Google Cloud Specialization in Machine Learning signal the strongest level of Google Cloud ML proficiency and experience. Accenture, Atos, Deloitte Consulting, DoiT International, Quanitphi and Pythian have achieved those designations.

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Whether you’re newly entering the workforce, have been recently laid off, are worried about keeping your current job or have been temporarily furloughed and have some time on your hands, there’s no better time to pick up some AI-related skills than right now.

According to LinkedIn, artificial intelligence and machine learning jobs have grown 74% annually over the past four years. Job titles in this category include data scientists and machine learning engineers, but if you’re confused about the differences between a data scientist vs. machine learning engineer, you’re not the only one.

“To begin with, there was no distinction between the two roles,” said Pragyansmita Nayak, chief data scientist at Hitachi Vantara Federal, which provides technology services to federal agencies.

When the two jobs first started growing, companies advertised for data scientists whether the job was more on the data scientist vs. machine learning engineer side.

“That confusion [still] exists today,” Nayak said.

What is your background?

The biggest difference between a data scientist vs. machine learning engineer, experts said, is that they come from very different places.

“Data science has its foundations in statistics and in the business side,” said Justin Richie, data science director at Nerdery, a digital services consultancy.

For example, a data scientist working at a bank might be asked to find out why customers are leaving, he said. The data scientist would decide on what data and analytics are needed and come up with a way to identify customers who are likely to leave.

Machine learning engineers, however, come from the other direction — from software development.

“They’re more focused on the production of the models and embedding them into applications,” Richie said.

In the bank example, a machine learning engineer might take the model created by the data scientist and turn it into production code to embed into a mobile banking application. With that, the insights can become actionable, with the bank taking immediate steps to change the minds of customers looking to jump ship.

Key skills for data scientists

According to Ira Cohen, chief data scientist at Anodot, an autonomous business monitoring platform, “data scientist” is still often used as the overall umbrella term, with machine learning engineer being a narrower subset of it.

But, increasingly, data scientist is becoming a more specialized job category, analyzing business data using machine learning or artificial intelligence, he said. “Similar to the role of business analysts.”

Data scientists often start out as business analysts and boost their math and analytics skills with additional courses or on-the-job training. Some also start out right in data science, with academic backgrounds in statistics or artificial intelligence.

In addition to math and business domain knowledge, data scientists typically need programming skills to be able to develop prototypes of their models. R and Python are the most common programming languages for the job, but Scala, Julia, JavaScript, Swift, Matlab and Go can also be useful. Data scientists should also be familiar with data visualization tools like Power BI, Tableau and Qlik.

Andrew Stevenson, CTO at Lenses.io, a company that offers data platform monitoring technology, once worked on a project with data scientists from an energy trading desk.

“They were able to build the models, test and run locally,” Stevenson said. And then they hit the limit of their expertise, he said. “The models were not production-grade. They had no monitoring, they weren’t version controlled, they were not easily developed in a repeatable way. They were black boxes and if the desktop got rebooted, they had a production incident.”

This is the point where machine learning engineers step in.

“Data scientists are typically mathematical but literate in programming,” Stevenson said. “Data scientists in a financial trading firm — the quants — usually have Ph.D.s in mathematics but are also technically savvy with tooling such as R and Matlab, but they rely on highly skilled, hard-to-find programmers to implement their algorithms and bring them to production.”

Key skills for machine learning engineers

Machine learning engineers typically start out on the software development side and add machine learning skills through on-the-job training or additional study, though some are now graduating from specialized degree programs.

It’s never been easier for a software developer to become a machine learning engineer, said Sachin Gupta, co-founder and CEO at HackerEarth.

“With more and more open source libraries from tech giants like TensorFlow from Google bringing pretrained models for various use cases, it’s becoming simpler for machine learning engineers to experiment with a multitude of models,” he said.

Then the machine learning engineer deploys these models, builds APIs and web interfaces, and builds data pipelines, he said.

Machine learning engineers have some overlap with data scientists in terms of skills. Both may be using R or Python, for example, and both need advanced math skills like linear algebra and statistics.

But machine learning engineers are expected to be more highly skilled when it comes to programming, said Alex Ough, senior architect CTO at Sungard Availability Services. Machine learning engineers also need to know production platforms such as AWS, Azure and GCP and their AI services, he said.

Where the 2 jobs overlap

Larger companies typically see data scientists and machine learning engineers as two separate job functions, Richie said. But at smaller and midsize companies, one person may be doing both jobs, with the company hiring either one or the other. That can be a mistake, he said.

“Hiring a single person to do all of those things is not signing up that person for success,” Richie said.

He suggested that companies that can only afford to hire one person, hire for the specific job they need the most.

“Then cross-train other people at the company,” he said. “That’s what I’ve been advising the customers we work with. For example, business analysts are good for learning data science skills. Use the existing skill sets and hire only the specific niche vertical that you need.”

Working hand in hand

For companies that hire both data scientists and machine learning engineers, the two typically work closely on projects.

“Think of it as a data scientist being the architect of a building,” Nayak said. “And the machine learning engineer is the general contractor who actually builds the building.”

Data scientists start out with the data, the goals and the algorithms, she said, while the machine learning engineer starts with the code. But the two work together on many tasks. Data scientists usually choose the best machine learning algorithm for a particular project, but machine learning engineers have a better idea about the frameworks used by the organization.

“I would talk to the machine learning engineer,” Nayak said. “I would ask what the different options are, what they would recommend.”

Then, after the machine learning engineer has done the development work and put the application into a production environment, the data scientist may be needed again.

“That’s where the data scientist goes back to the end users and works with them and makes sure they are comfortable with the systems,” Nayak said.

How to get the skills

Many online educational platforms are making courses available for free or at a low cost, including those that offer credentials that can be added to a resume. And, to put those skills to some use, there are volunteer opportunities available to write code for nonprofits or help with open source projects.

Even during the pandemic, there are opportunities to network. Many local data science groups and larger conferences and other events are going virtual. Look for opportunities to do presentations about your ongoing projects, find volunteer opportunities and pick up industry gossip about who’s hiring.

Data science is not pandemic proof

At first, it seemed people working in AI and analytics would be spared from the brunt of this crisis. After all, these are jobs that can very easily transition to a work-from-home model.

According to a survey by recruiting firm Burtch Works that was conducted in partnership with the International Institute for Analytics, in the first week of the social distancing in the U.S. 56% of respondents said that they saw no staffing impact as a result of the pandemic. But by the third week, only 40% of respondents could say the same and 38% reported “some or substantial” cuts at their companies.

There’s also been a dramatic decline in LinkedIn data scientist job postings, Burtch Works reported. Listings went from more than 21,000 postings in late March to fewer than 17,000 a month later — though the listings rebounded somewhat to 18,000 job postings by mid-April.

Long term, however, there is significant optimism about this sector, so you may want to consider getting training to become a data scientist or a machine learning engineer or improve the skills you already have.

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