Custom Software Built on Assembly Line - Engineer AI

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.

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.”

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.

Also Read : Engineer.ai launches Builder app for customized digital products

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.

Read Full Article @ https://www.businesstoday.in/magazine/the-buzz/custom-software-built-on-assembly-line/story/278877.html

Knowledge Engineering in AI - Definition, Process

What is Knowledge Engineering?

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 Image illustrates the knowledge engineering pipeline.

Knowledge Engineering Processes

In terms of its role in artificial intelligence (AI), knowledge engineering is the process of understanding and then representing human knowledge in data structures, semantic models (conceptual diagram of the data as it relates to the real world) and heuristics (rules that lead to solution to every problem taken in AI).

Expert systems, and algorithms are examples that form the basis of the representation and application of this knowledge.
The knowledge engineering process includes:

• Knowledge acquisition
• Knowledge representation
• Knowledge validation
• Inferencing
• Explanation and justification

The interaction between these stages and sources of knowledge is shown in Figure below.
 

The amount of collateral knowledge can be very large depending on the task. A number of advances in technology and technology standards have assisted in integrating data and making it accessible.

These include the semantic web (an extension of the current web in which information is given a well-defined meaning), cloud computing (enables access to large amounts of computational resources), and open datasets (freely available datasets for anyone to use and republish). These advances are crucial to knowledge engineering as they expedite data integration and evaluation.

Read Full Article @ https://study.com/academy/lesson/knowledge-engineering-in-ai-definition-process-examples.html

Kevin Carmichael taking economic growth seriously?

Kevin Carmichael: While he does have a policy idea that would actually make a difference, Jason Kenney isn't running for PM just yet

Finally someone campaigning to become prime minister has proposed to do something that might actually change the trajectory of the economy.

Alas, that someone is Alberta Premier Jason Kenney, who only secured his current job five months ago, and won’t be pursuing the position that every pundit says he really wants until 2024 at the earliest.

Whatever his aspirations, Kenney is the only national political figure who has used the federal election campaign to show an interest in doing something about crippling productivity rates. His pledge on the weekend to drop some inter-provincial trade barriers in order to create momentum for talks on making Canada a proper single market could be a game changer. It also comes with a note of seriousness that so many previous commitments lacked.

Here’s the prize: The International Monetary Fund said earlier this year that freer internal trade would increase per capita gross domestic product by about four per cent. It was an astonishing finding, given several studies predict the new North American trade agreement will have essentially no effect on GDP. It makes you wonder what could happen if the country’s business, political and media elite united in excitement over a Royal Rumble within the federation the way they obsessed over last year’s confrontation between Team Trudeau and the Trump administration.

Read full story @ https://business.financialpost.com/news/election-2019/want-to-know-who-is-taking-long-term-economic-growth-seriously-hint-he-doesnt-lead-a-national-party