• Latest
  • Trending
With Machine Learning, More Business Processes Will be Automated 

With Machine Learning, More Business Processes Will be Automated 

September 30, 2021
Just-In: Ethereum Merge Most Likely In August, Says Vitalik Buterin

Just-In: Ethereum Merge Most Likely In August, Says Vitalik Buterin

May 20, 2022
Trader Predicts Crypto Market Will Mimic 2018 Bear Season – Here’s How High Bitcoin Could Go Before Nuking Lower

Trader Predicts Crypto Market Will Mimic 2018 Bear Season – Here’s How High Bitcoin Could Go Before Nuking Lower

May 20, 2022
Terraform Labs, Luna Foundation Guard Bought 3.06m AVAX in total: Avalanche Foundation

Terraform Labs, Luna Foundation Guard Bought 3.06m AVAX in total: Avalanche Foundation

May 20, 2022

TD SYNNEX expands solution offering with Google Cloud

May 20, 2022

Creating an ML Web App and Deploying it on AWS

May 20, 2022
Will Fan Tokens Replace Memecoins Like Shiba Inu and Dogecoin?

Will Fan Tokens Replace Memecoins Like Shiba Inu and Dogecoin?

May 20, 2022
Goldman Sachs: Crypto Drawdown Will Have Little Impact on U.S. Economy

Goldman Sachs: Crypto Drawdown Will Have Little Impact on U.S. Economy

May 20, 2022
Crypto Bear Market: Pantera Partner Sees These Buying Opportunities

Crypto Bear Market: Pantera Partner Sees These Buying Opportunities

May 20, 2022
Australias Commonwealth Bank Halts Crypto Rollout

Australias Commonwealth Bank Halts Crypto Rollout

May 20, 2022
Commonwealth Bank puts crypto trading trial on ice as regulators dither

Commonwealth Bank puts crypto trading trial on ice as regulators dither

May 20, 2022
Ethereum devs tip The Merge will occur in August ‘if everything goes to plan’

Ethereum devs tip The Merge will occur in August ‘if everything goes to plan’

May 20, 2022
Beware, Bitcoin Jumping Back Above $30,000 Could Be A Dead Cat Bounce, Here’s why

Beware, Bitcoin Jumping Back Above $30,000 Could Be A Dead Cat Bounce, Here’s why

May 20, 2022
Deep Tech Central
Tuesday, June 28, 2022
Subscription
Sign Up
  • News
    • Artificial Intelligence
    • Crypto
    • CyberSecurity
    • IoT
    • Robotics
    • Quantum Computing
    • Sustainability
    • Telecom
  • Videos
  • DTC – UNV
No Result
View All Result
Deeptech Central
No Result
View All Result

With Machine Learning, More Business Processes Will be Automated 

by DeepTech Central
September 30, 2021
in Artificial Intelligence
0

By AI Trends Staff  

Machine learning has the potential to automate many more business processes than are currently automated in enterprise software, based on all the previous generations of software development methods.   

YOU MAY ALSO LIKE

Creating an ML Web App and Deploying it on AWS

Now You Don’t Need To Present Your Credit Card At Checkout If You Bind Your Facial Images/ Hand Features To Your MasterCard Credit Card

That is a suggestion put forward by Claus Jepsen, chief technology officer at Unit4, an ERP software supplier based in Denmark.    

Claus Jepsen, chief technology officer, Unit4 of Denmark

“Based on my experience, typically less than 20% of business processes are automated in enterprise software. I believe that in as little as two to three years, we could see up to 80% of routine business processes automated by ML,” Jepsen stated in a recent account in Forbes. 

Much of machine learning, which he describes as the ability to create automation through AI algorithms, is statistical analysis from crunching numbers, identifying patterns and predicting future outcomes based on past results. All this can be done with standard logical programming.   

The degree to which ML can improve the business outcomes is “currently marginal,” he suggests with accuracy of financial forecasts, for instance, sensitive to many greater factors than how well the algorithm can refine itself over time. “If you haven’t got harmonized, accurate and complete data to start with, simply applying ML to it isn’t in itself going to result in better business decisions,” Jepsen stated. 

Defining the business problem is the same challenge that has always faced software developers. “In terms of Gartner’s hype cycle, ML is currently at the peak of inflated expectations,” he stated. “You cannot simply throw ML at a bucket of big data and expect it to magically come up with a perfect business plan.”  

The points in a business process where some judgment or prediction is required, and where a small improvement in accuracy would have a strong benefit to the business, are candidates for ML automation. The humans surrounding the effort to get AI to work are critical. They need to decide the use case and make sure the data is of high enough quality to be useful, before giving the algorithm a task, and then training it.   

“The human mind is by far the best pattern-matching machine in the universe,” Jepsen stated. “The average two-year-old can probably correctly identify a cat after it’s seen two or three, while an ML algorithm might need to see 2,000 before it can be sure. But, once trained, ML excels at dealing with huge volumes of data and processing it very quickly, while never getting bored performing repetitive, tedious tasks day in, day out.” 

Machine Learning Catching on in Africa  

This insight of machine learning extending automation beyond what software development has so far achieved, extends to Africa, where machine learning is making gains. IDC analysts have projected that spending on AI in the Middle East and Africa is expected to maintain its strong growth trajectory as businesses continue to invest in projects that use AI software and platforms, according to an account in Intelligent CIO Africa.   

AN IDC survey of IT leaders found that ML improved customer and employee experience and led to accelerated rates of innovation in the organization.  

Fady Richmany, senior director and general manager, UAE Dell Technologies

The same challenges apply: pick a good candidate business problem to automate with ML, and make sure the data is available to make it work. As part of this, “Identifying and understanding whether the problems they are trying to solve could be tackled better and more accurately by Machine Learning rather than conventional software is key,” stated Fady Richmany, senior director and general manager, UAE Dell Technologies. 

Speaking of candidate applications for ML, Richmany stated, “Enterprises can use Machine Learning for customer retention, since ML systems can study customer behavior and identify potential steps for customer retention. Additionally, they can make use of ML to help with market research and customer segmentation, allowing them to deliver the right products and services at the right time, while also gaining valuable insights into the purchasing patterns of specific groups of customers to better target their needs.”  

ML Platform Buy or Build Considerations  

Companies that commit to pursuing machine learning for AI software development face a decision on whether to buy or build the needed ML platform. 

“Building a solution takes years and headcount,” states Charna Parkey, data science lead at Kaskada of Seattle, in a recent account in builtin. Kaskada is building a machine learning platform aimed at enabling collaboration on feature engineering and repeatable success in production.  

Airbnb for example took three months to decide what to build in their ML platform and four years to build it; they call it Bighead. Its developers used a range of open source technologies, working to “fix the gaps in the path to production” with their own services and user interface. This meant they had to support multiple frameworks, feature management and model and data transformation. In a similar experience, Uber has been working for five years on its platform, called Michaelangelo. And Netflix started more than four years ago on its platform, which continues in development, according to Kaskada. 

Finding the needed talent is always a challenge. The basic decision is whether to hire a classically trained data scientist, or hire a domain expert and upskill. “I chose to upskill,” Kaskada stated, and she is not alone. Some 46% of organizations surveyed by PwC in 2020 reported they were rolling out AI upskilling to handle the shift to more AI, and 38% were implementing credentialing programs.   

Buying a pre-built ML platform saves the initial costs to build, the integration costs for “custom, brittle workflows,” and it comes with dedicated external support, she stated. It also reduces the time it takes to onboard new employees to proprietary software. The costs of moving to a pre-built platform including having to adopt new workflows instead of building to those the company has in place, and perhaps telling developers their favorite tools are no longer in vogue. 

“Not all platforms will support the entirety of your ML operations or your company’s unique needs,” Kaskada suggested. “Evaluate carefully.” 

New Book: Real World AI: A Practical Guide for Responsible Machine Learning  

In the real world of applied ML applications, the challenges are just beginning to be understood, suggest the authors of a new book, Real World AI: A Practical Guide for Responsible Machine Learning, by Alyssa Simpson Rochwerger and Wilson Pang, two experienced practitioners of applied machine learning. Rochwerger is a former director of product at IBM Watson, and Pang is the CTO of Appen, a company focused on improving the quality of data for ML applications, based in Chatswood, Australia. 

“Only 20% of AI in pilot stages at major companies make it to production, and many fail to serve their customers as well as they could,” Rochwerger and Pang write in Real World AI, according to an account of the book recently published in TechTalks. “In some cases, it’s because they’re trying to solve the wrong problem. In others, it’s because they fail to account for all the variables—or latent biases—that are crucial to a model’s success or failure.”  

The real world clashes with the academic roots of AI when it comes to data.  

“When creating AI in the real world, the data used to train the model is far more important than the model itself,” Rochwerger and Pang write in Real World AI. “This is a reversal of the typical paradigm represented by academia, where data science PhDs spend most of their focus and effort on creating new models. But the data used to train models in academia are only meant to prove the functionality of the model, not solve real problems. Out in the real world, high-quality and accurate data that can be used to train a working model is incredibly tricky to collect.”  

Read the source articles and information in Forbes, in Intelligent CIO Africa, in builtin and in TechTalks. 

Share196Tweet123Share49

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

I agree to the Terms & Conditions and Privacy Policy.

Search

No Result
View All Result

Recent News

  • Just-In: Ethereum Merge Most Likely In August, Says Vitalik Buterin
  • Trader Predicts Crypto Market Will Mimic 2018 Bear Season – Here’s How High Bitcoin Could Go Before Nuking Lower
  • Terraform Labs, Luna Foundation Guard Bought 3.06m AVAX in total: Avalanche Foundation
  • About
  • Privacy Policy
  • Sign Up
  • Contact Us
  • About
  • Contact
  • Deeptech Central
  • Elementor #10628
  • Newsletter
  • Privacy Policy
  • Sign Up

© 2018-2021 DeepTech Central. - by MintMore Inc..

No Result
View All Result
  • News
    • Artificial Intelligence
    • Crypto
    • CyberSecurity
    • IoT
    • Robotics
    • Quantum Computing
    • Sustainability
    • Telecom
  • Videos
  • DTC – UNV

© 2018-2021 DeepTech Central. - by MintMore Inc..

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy and Cookie Policy.

Stay Updated. Subscribe Today.

Join the community of 10K+ scholars & entrepreneurs.