Sunday, April 11, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Machine Learning

How to overcome AI and machine learning adoption barriers | AI

May 10, 2020
in Machine Learning
How to overcome AI and machine learning adoption barriers | AI
585
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

Matt Newton, Senior Portfolio Marketing Manager at AVEVA, on how to overcome adoption barriers for AI and machine learning in the manufacturing industry

There has been a considerable amount of hype around Artificial Intelligence (AI) and Machine Learning (ML) technologies in the last five or so years.

So much so that AI has become somewhat of a buzzword – full of ideas and promise, but something that is quite tricky to execute in practice.

You might also like

Can a Machine Learning Model Predict T2D?

Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU

IBM releases Qiskit modules that use quantum computers to improve machine learning

At present, this means that the challenge we run into with AI and ML is a healthy dose of scepticism.

For example, we’ve seen several large companies adopt these capabilities, often announcing they intend to revolutionize operations and output with such technologies but then failing to deliver.

In turn, the ongoing evolution and adoption of these technologies is consequently knocked back. With so many potential applications for AI and ML it can be daunting to identify opportunities for technology adoption that can demonstrate real and quantifiable return on investment.

Many industries have effectively reached a sticking point in their adoption of AI and ML technologies.

Typically, this has been driven by unproven start-up companies delivering some type of open source technology and placing a flashy exterior around it, and then relying on a customer to act as a development partner for it.

However, this is the primary problem – customers are not looking for prototype and unproven software to run their industrial operations.

Instead of offering a revolutionary digital experience, many companies are continuing to fuel their initial scepticism of AI and ML by providing poorly planned pilot projects that often land the company in a stalled position of pilot purgatory, continuous feature creep and a regular rollout of new beta versions of software.

This practice of the never ending pilot project is driving a reluctance for customers to then engage further with innovative companies who are truly driving digital transformation in their sector with proven AI and ML technology.

Innovation with direction

A way to overcome these challenges is to demonstrate proof points to the customer. This means showing how AI and ML technologies are real and are exactly like we’d imagine them to be.

Naturally, some companies have better adopted AI and ML than others, but since much of this technology is so new, many are still struggling to identify when and where to apply it.

For example, many are keen to use AI to track customer interests and needs.

In fact, even greater value can be discovered when applying AI in the form of predictive asset analytics on pieces of industrial process control and manufacturing equipment.

AI and ML can provide detailed, real-time insights on machinery operations, exposing new insights that humans cannot necessarily spot. Insights that can drive huge impact on businesses bottom line.

AI and ML is becoming incredibly popular in manufacturing industries, with advanced operations analysis often being driven by AI. Many are taking these technologies and applying it to their operating experiences to see where economic savings can be made.

All organisations want to save money where they can and with AI making this possible.

These same organisations are usually keen to invest in further digital technologies. Successfully implementing an AI or ML technology can significantly reduce OPEX and further fuel the digital transformation of an overall enterprise.

Industrial impact

Understandably, we are seeing the value of AI and ML best demonstrated in the manufacturing sector in both process and batch automation.

For example, using AI to figure out how to optimize the process to achieve higher production yields and improve production quality. In the food and beverage sectors, AI is being used to monitor production line oven temperatures, flagging anomalies – including moisture, stack height and color – in a continually optimised process to reach the coveted golden batch.

The other side of this is to use predictive maintenance to monitor the behaviour of equipment and improve operational safety and asset reliability.

A combination of both AI and ML is fused together to create predictive and prescriptive maintenance. Where AI is used to spot anomalies in the behavior of assets and recommended solution is prescribed to remediate potential equipment failure.

Predictive and Prescriptive maintenance assist with reducing pressure on O&M costs, improving safety, and reducing unplanned shutdowns.

Technological relations

Both AI, machine learning and predictive maintenance technologies are enabling new connections to be made within the production line, offering new insights and suggestions for future operations.

Now is the time for organisations to realise that this adoption and innovation is offering new clarity on the relationship between different elements of the production cycle – paving the way for new methods to create better products at both faster speeds and lower costs.

Credit: Google News

Previous Post

Zoom to implement additional security and privacy measures after NYAG investigation

Next Post

Chinese APT group Naikon targeted Western Australia government

Related Posts

Can a Machine Learning Model Predict T2D?
Machine Learning

Can a Machine Learning Model Predict T2D?

April 11, 2021
Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU
Machine Learning

Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU

April 10, 2021
IBM releases Qiskit modules that use quantum computers to improve machine learning
Machine Learning

IBM releases Qiskit modules that use quantum computers to improve machine learning

April 10, 2021
One-stop machine learning platform turns health care data into insights | MIT News
Machine Learning

One-stop machine learning platform turns health care data into insights | MIT News

April 10, 2021
Machine learning: is there a limit to technological patents in Brazil?
Machine Learning

Disclosing AI Inventions – Part I: Identifying the Unique Disclosure Issues

April 10, 2021
Next Post
Chinese APT group Naikon targeted Western Australia government

Chinese APT group Naikon targeted Western Australia government

Leave a Reply Cancel reply

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

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Can a Machine Learning Model Predict T2D?
Machine Learning

Can a Machine Learning Model Predict T2D?

April 11, 2021
Leveraging SAP’s Enterprise Data Management tools to enable ML/AI success
Data Science

Leveraging SAP’s Enterprise Data Management tools to enable ML/AI success

April 11, 2021
Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU
Machine Learning

Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU

April 10, 2021
Vue.js vs AngularJS Development in 2021: Side-by-Side Comparison
Data Science

Vue.js vs AngularJS Development in 2021: Side-by-Side Comparison

April 10, 2021
IBM releases Qiskit modules that use quantum computers to improve machine learning
Machine Learning

IBM releases Qiskit modules that use quantum computers to improve machine learning

April 10, 2021
Hackers Tampered With APKPure Store to Distribute Malware Apps
Internet Privacy

Hackers Tampered With APKPure Store to Distribute Malware Apps

April 10, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Can a Machine Learning Model Predict T2D? April 11, 2021
  • Leveraging SAP’s Enterprise Data Management tools to enable ML/AI success April 11, 2021
  • Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU April 10, 2021
  • Vue.js vs AngularJS Development in 2021: Side-by-Side Comparison April 10, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates