#engineer-ai

What does an Engineer AI do?

AI Engineers learn to develop and test complex algorithms, thus allowing a computer program to mimic gradual human characteristic approaches. Naturally, a job involving lines of code that makes it possible for software to imitate human like patterns, is a job involving a lot of inherent trial and error.The people who engineer AI thus happen to be highly trained professionals; they however, rarely receive credit for the work they do. Artificial Intelligence surrounds us – it engulfs nearly every sphere of our existence, like telecommunication and medicine.Skeptics though, have their eyes peeled – they foretell of the obsolescence of humankind, at the claws of robotic superiors. Is their mistrust towards Artificial Intelligence justified; especially when AI has managed to empirically do more good, than harm?

The Fear of An Alien Intelligence

Everyone benefits from a computer assisted service once a while. However, some choose to be neglectful of this good in favor of unfounded xenophobia. Simply put, it’s the semi-irrational fear that robots will eventually steal our jobs. While it is no secret that automation has taken over the sectors like manufacturing, the quality of jobs that robots have taken over happens to be labor-intensive and menial. To be fair, the last decade or so has seen unprecedented growth in AI and robotics – much of it has taken existing industries by surprise. This has caused a huge chunk of the labor force to be displaced, which stands at the crux of a lot of economic anxiety being experienced. No wonder there is an entire stratum of employees that feel isolated due to the rising trends in AI.

Folks that Engineer AI, fear that such a line of thinking discredits the monumental work that they do, which propagates the unsubstantiated claim that soon, robots will come to dominate every industry, rendering the presence of humans, rather moot. According to a Pew research, nearly 70% of Americans are wary about the rising trend of machines in everyday life.

However, such threats seem farfetched (or not as imminent as science fiction would have you believe) once you grasp the ground reality – the AI army happens to be severely understaffed!

Those Who Engineer AI Are Few And Far Between

A glass half-full situation is threatening the projected progression curve,within the world of AI. Nearly a million openings for individuals who Engineer AI is required, only 300,000 odd candidates however, cap out the entire workforce. Apart from posing a problem for the industries where automation has been partially or fully implemented (Banking, Manufacturing, etc.),potentially causing arrested development; it is actively limiting prospective implementation toward spressing issues,such as global warming and waste disposal. Established industries are threatened with decelerated growth, while potentially implementable sectors would be left astray.

The field of Artificial Intelligence did not come to be overnight. Thousands of pioneers showed up to work every day, while managing to put in hard work for decades on end. Much like a medical professional, the average AI Engineer too, is subjected to years of grueling studying and testing. Not only does the field demand an advanced understanding of mathematics and programming, an AI Engineer is also expected to have an innate understanding of Machine Learning, Neural Networks, and Robotics, among others.

The sector, however, remains in dire need of fresh professionals. A 2019 study by the Global AI Talent Report said that there were only 22,400 top AI Academics in the whole world. Among these, only about 10,000 were found to be truly highly skilled. Simply put, Artificial Intelligence isn’t firing on all cylinders, but instead dragging its feet to the finish-line.

Lack Of AI Engineers – A Double Edged Sword?

The dearth of professionals that engineer AI is limiting the optimization within various industries. But is it a self-restraint that must be adopted, for the sake of humanity in the future?

Knowledge engineering is one of the building blocks of artificial intelligence (AI). It attempts to emulate the judgment and behavior of a human with expertise in a field or domain. In this lesson, we'll define knowledge engineering and its processes, understand where it fits within the AI landscape, and provide real-world examples of its application.

Imagine an education company wanting to automate the teaching of children in subjects from biology to computer science (requiring to capture the knowledge of teachers and subject matter experts) or Oncologists choosing the best treatment for their patients (requiring expertise and knowledge from information contained in medical journals, textbooks, and drug databases).

Knowledge Engineering is the process of imitating how a human expert in a specific domain would act and take decisions. It looks at the metadata (information about a data object that describes characteristics such as content, quality, and format), structure and processes that are the basis of how a decision is made or conclusion reached. Knowledge engineering attempts to take on challenges and solve problems that would usually require a high level of human expertise to solve. Below Images illustrates the knowledge engineering pipeline.

If you think Artificial Intelligence (AI) will kill our Engineers jobs, then you are totally wrong.

Similar to how our jobs have moved and shifted today considering that new generation has been introduced, so too will shifting arise for jobs of the future. AI and generative design might also update some of the need for designers, draftsmen, engineers, and so on, but our jobs will shift into new areas with as a lot of potential as before.

In case of Civil Engineers, imagine a world where AI automatically preps your survey information and lays the groundwork for a brand new site within CAD. It means that the civil engineer could spend more time in designing, optimizing, and working with the client to ensure everyone is satisfied.

For Mechanical Engineers, imagine a world where a machine can generate your components for you, and all you need to do is optimization and select components on a grander scale.

With the development of technology, our jobs will just change. Normally, AI will improve the quality of work-life and design, and ultimately improve our ability to be the best engineers that we can be.

1) The Founders

Sachin Dev Duggal and his college mate Saurav Dhoot. Duggal, a serial entrepreneur, holds an engineering degree from Imperial College, London, and a master's degree in entrepreneurship from MIT. The duo set up their first company Nivio in 2004. They cashed out in 2012 and started SD Squared that finally evolved into Engineer.ai.

2) The Idea

"A decade ago, 'tech' was just a tag, but now, it is a must-have," says Duggal. "However, for millions of SMBs in India and elsewhere, buying expensive software or hiring in-house IT teams is not a viable option. So, we have built tech solutions to turn ideas into products at double the speed and tailored pricing."

3) How It Works

Customers can specify their concepts with the help of a drag-and-drop features menu on a human-assisted and AI-powered cloud platform called Builder. Here applications are developed in an assembly line manner to ensure speed, scale and cost efficiency. Simply put, each project is broken down into building blocks, and up to 60 per cent of the work is done by AI tools and automated processes. As most of the applications have standard features and repeat codes, these are automatically assembled in projects as per requirements and customers only pay for unique coding. The 'custom' part of the work is allocated to domain experts from a network of 50 software firms and 10,000 developers. Support and maintenance are provided for a monthly fee.

The start-up is also into cloud arbitrage and offers CloudOps, an AI-based cloud management platform for businesses to ensure smart usage and cost benefits. Plus, there is a prepaid card for buying cloud storage capacity.

https://blog.engineer.ai/mistake-free-mvp/What is an MVP?

According to Wikipedia, a Minimum Viable Product (MVP) is, “a product with just enough features to satisfy early customers and provide feedback for future product development.” Another site, Techopedia, notes further it’s a, “development technique in which a new product or website is developed with sufficient features to satisfy early adopters. The final, complete set of features is only designed and developed after considering the feedback from the product’s initial users.”

Why release your MVP?

In the lean or agile model of product development, you want a product which your customers can test most importantly. It’s a representation of your brand that’s intended to give users a clear understanding what and how your product benefits their needs. As with any business, if you can help customers solve pain points they face regularly, you’ve found the sweet spot. The result is customer feedback before plunging all of your money into a final product–that may fail or at the very least miss the mark.

Let’s also consider the word minimum. What does it mean in this case? Here, “minimum” means basic or bare bones. You may have heard, “if you launch it and you’re not slightly embarrassed, you’ve launched too late.” And while we don’t completely agree–this is a great point.

Questions to ask yourself through the process

After getting your idea, your rough sketch of your idea in action provides your most basic MVP. But a table napkin MVP doesn’t allow your customers to see your idea in action. Consider the following questions

What’s the essential point of this application?

How does this software benefit my customers?

How do my customers describe this software helping them solve their pain point?

How can you translate their feedback into a revised MVP that you continue to improve on?