Question

INSTRUCTIONS:

After reading the attached article, How To Reskill Your Workforce For AI (Artificial Intelligence).pdf your job is to provide a very brief summary, analysis, and evaluation. We can define these important concepts as follows:

• Summary- A formal, logical, consistent way of highlighting the main points.
  • Purpose:
    • In school - to quickly and accurately describe something you have read
    • In professional life - to provide a faster-to-read version of the material to other readers
    • In personal life - to reflect as accurately as possible on people, events, and one's memories of them.
• Analysis- A taking apart of something to show its parts or pieces, often using a special system, theory, or set of theories.
  • Purpose:
    • In school - to think more about a subject and/or to apply the methods of an academic discipline to a specific text
    • In professional life - to apply a system or idea to a specific situation so that others understand how to use something
    • In personal life - to examine one's own thoughts, actions, and motives logically and consistently from a variety of perspectives.
• Evaluation- A judgment of the value of a text to society or the quality of the way it is argued or organized.
  • Purpose:
    • In school - to show how well or poorly something has been done, or its effects on others beyond its main ideas
    • In professional life - to help decide who to hire, how well people are doing, and the quality and style of your own work
    • In personal life - to look not so much at the contents of one's own thinking and acting, but rather at the quality and value of that thinking and acting.

Each response should be no less than 500 words and no more than 750 words. You should provide in-text citations (APA style) and a reference page (APA style).

How To Reskill Your Workforce For AI (Artificial Intelligence).pdf

ARTICLE:

AI is considered the most disruptive technology, according to Gartner’s 2019 CIO Survey (it includes over 3,000 CIOs from 89 countries). So yes, this is big reason why there has been a major increase in adoption and implementation.

Yet there is a bottleneck that could easily slow the progress – that is, finding the right talent. The fact is that there are few data scientists and AI experts available.

“In our recent State of Software Engineer report, we found that demand for data engineers has increased by 38% and demand growth for machine learning engineers has increased by 27% in the last year,” said Mehul Patel, who is the CEO of Hired. “Based on data from our career marketplace, we believe the difficulty of recruiting for tech talent with specialized skills in machine learning and AI will continue to become increasingly competitive. Machine learning engineers are commanding an average salary of 153K in the SF Bay Area, which is nearly 20K above the global tech worker’s average salary.”

Actually, this is why one approach is to acquire companies that have strong teams! This appears to be the case with McDonald’s, which recently paid $300 million for Dynamic Yield. It’s an AI company that helps personalize customer experiences.

But of course, this option has its issues as well. Let’s face it, acquisitions can be difficult to integrate, especially when the target has a workforce with highly specialized skillsets.

So what are other approaches to consider? Well, here’s a look at some ideas:

Automation: With the growth in AI, there has also been the emergence of innovative automation tools, whether from startups or even the mega tech operators. For example, this week Microsoft introduced a new set of systems to streamline the process.

“The biggest and most impactful way that organizations can leverage their current team for data science is to implement a data science automation platform,” said Dr. Ryohei Fujimaki, who is the founder and CEO of dotData. “Data science automation significantly simplifies tasks that formerly could only be completed by data scientists, and enables existing resources -- such as business analysts, BI engineers and data engineers -- to execute data science projects through a simple GUI operation. Automation of the full data science process, from raw business data through data and feature engineering through machine learning, is enabling enterprises to build effective data science teams with minimal costs, using their current talent.”

Now this does not mean that a platform is a panacea, as there still needs to be qualified data scientists. But then again, there will be far more efficiency and scale with AI projects.

“If organizations have data scientists already, an automation platform frees up highly-skilled resources from many of the manual and time-consuming efforts involved, and allows them to focus on more complex and strategic analysis,” said Ryohei. “This empowers data scientists to achieve higher productivity and drive greater business impact than ever before.”

Reskilling: If you currently have employees who are business analysts or have experience with data engineering, then they could be good candidates to train for AI tasks. This would include focusing on skills like Python and TensorFlow, which is a deep learning framework.

“From a training and learning perspective, there are an abundance of online resources via Coursera, Udacity, open.ai, and deeplearning.ai that can help companies develop their employees’ AI/ML skills,” said Mehul. “Additionally, it will be valuable for a company to acquire someone with existing experience in AI to be a leader and mentor for developing employees. The interesting thing about AI/data science is that you don't need to be an experienced software engineer to do it. The field is so exciting because of the diversity of talent and backgrounds spanning science, engineering, and economics.”

But the training should not just be for a small group of people. It should be company-wide. “Without a data-driven culture and mindset, data science and AI cannot be truly implemented,” said Ryohei. “It is important for enterprise leaders and business teams to understand how to best work with the data science team to meet the organization’s key business objectives. While the business stakeholders do not need to be data experts, they need to know ‘How to use’ AI and ‘How it changes their businesses.’”

Answer 1

HOW TO RESKILL YOUR WORKFORCE FOR AI (ARTIFICIAL INTELLIGENCE)

SUMMARY-

Artificial Intelligence is impacting the technology and is being increasingly adopted worldwide. Gartner's 2019 CIO survey states that there are 3000 CIO's from 89 countries involved in AI. In state of software engineer report Mehul Patel found out there is increasing demand of 38% data engineers and 27% machine learning engineers. All over world there are only few data scientists and AI experts are available. The recruitment of tech talent people specialized in machine learning and AI is becoming competitive day by day.

McDonald's is an AI company which personalized customer experiences and has paid $300 million for Dynamic yield.

Now this process has some problems or consider it as dark sides so we can see other approaches :

Automation: The startups and the mega tech operators have demand of innovative automation tools like Microsoft introduced a new set of sysytems to strealime the process.

As stated by Dr. Ryohei Fujimaki, founder and CEO of dotData- the company has the opportunity to leverage their current team for data science is to implement a data science automation platform. The problems faced by data scientists are being solved by mergence of data science automation as it enables existing resouces such as business analyst, Business intelligence engineers and data engineers. Data science automation helps these engineers to tackle data science projects through simple GUI operation. Data Science automation helps in processing of raw business data, feature engineering through machine language is useful to build effective data science team with minimum cost.

With data science higher productivity can be achieved by freeing data scientists from manual and time consuming tasks nad involving them more in complex and strategic analysis.

Reskilling: If any business has employees who are good in business analyst the can be trained for AI tasks with less efforts.Different softwares like coursera, udacity, open.ai, and deeplearning.ai can be used to train company employees AI/ML skills.

ANALYSIS -

Analyzing the pdf I found out AI (Artificial Intelligence ) is the emerging technology which is replacing every system making all decisons end to end and enchancing every specific process. Artificial Intelligence rfers to the simulation of human intelligence in machines, in this machines are made to work like humans through problem solving and decision making capabilities.

Survey of Gartner's 209 CIO show that there are 3000 CIO from 89 countries are involved in this technology. This shows that there only few who have good command in AI. Artificial ntelligence helps company by improving productivity and the bottom line at every stage of business lifecycle. It has a competitive advantage, cost and time saving , the technology is altering the business process of every size of company.

Data Science Automation is process of extracting knowledge and infromation from data which include machine learning and AI. Every company with AI requires innovative automation tools that can be utilised by data scientist to save their time from manual task and can invest their time in complex and startegic tasks. Data science helps in peocessing of raw business data.

Every company should move forward and train their business employees to learn python and tensorflow which are deep learning framework. This will enchance artificial intelligence/machine language skills of employees.

EVALUATION-

In the world there are only few techies who have strong command in Artificial intelligence, now when we see the growth of artificial intelligence there is demand of more people who know machine laguage. Data engineers and machine engineers are continously in demand as the process is becoming more reliable on this two fields.

Data sceince is the stream where different techies like business intelligence engineers, business analyst, machine language engineers can be trained to learn innovative automation tools.