Machine Learning

Machine Learning
What do you see as the biggest obstacle to undertaking AI projects in your company?

Top Answer : Quality of data over sufficient

1323 views
6 comments
4 upvotes
Related Tags
Are chatbots AI (Artificial Intelligence) or ML (Machine Learning)?

Top Answer : I don't like the word chatbot per se. Whether it's machine learning or AI, there are steps that you need to go through to train whichever intelligent assistant or whatever AI model you're working on. You need a number of cases and you have to properly train your model with a human in the middle, because the only way that AI can learn is if you actually identify what is not correct to help the model to ensure accuracy. One of the examples that my seed investment was working on was that the AI must know how to differentiate cucumbers from tomatoes. If a model looks at a cucumber and labels it as a tomato, the human in the middle corrects that mistake.

32 views
10 comments
0 upvotes
Related Tags
Does machine learning (ML) use artificial intelligence (AI) or does AI use ML?

Top Answer : I see ML as something that's actually looking at my dataset and my environment, understanding what's going on and then surfacing that data in a way that is helpful—maybe in a way I didn't know I needed that data. The AI is the conversational part that is acting on my behalf, executing whatever I want it to execute. That's where I make that separation. I'm much more excited about the ML data side of things than I am about AI at this point.

Innovation:  AI Investment and AdoptionInnovation: AI Investment and Adoption

AI/ML investments are well underway at leading companies and show no signs of slowing. But are these massive investments generating the business results they promise? Your IT peers tell all.

Machine Learning and Inference model monitoringMachine Learning and Inference model monitoring

As machine learning becomes more important to businesses, how are they thinking about Inference model monitoring? Is it a priority for them?

0 views
Related Tags
Thinking about deep machine learning, how ready is your data (cleaned, prepped and labeled) for compute-heavy data models?

Top Answer : Probably depends on age and size of business. Ours has divisions over 100 years old or acquired less than 2 years ago. We struggle with data quality even within a division. Note that our smallest is probably 25,000 employees and largest 40,000. Total company 130k people, 15 major ERPs and probably 20 minor ERPs. We still have debates on Customer address and shipping fields sometimes during consolidations. My estimate 35% of the data we would like to use is ready

What are your thoughts on SaaS management platforms (SMP)?

Top Answer :

11 views
0 comments
2 upvotes
Related Tags
Business Application Development
Architecture & Strategy
Maintenance
Requirements & Design
Testing, Deployment & QA
Mobile Development
Development
Selection & Implementation
Business Analysis
Applications Vendor Landscapes
Optimization
Backup
Data Center
Public and Hybrid Cloud
Telephony
Network
Compute
Storage
Business Applications
Cloud
Crisis Management
Data & Business Intelligence
Artificial Intelligence
Business Intelligence Strategy
Data Management
Enterprise Integration
Integrations
Machine Learning
Governance
Data Lake
Big Data
Data Warehouse
Disruptive & Emerging Technologies
5G
Blockchain
Cryptocurrencies
Virtual Reality
IoT
Reality
Digital Innovation
Bots
Augmented Reality
End-User Services & Collaboration
Collaboration solutions
End User Equipment
End-User Computing Devices
Endpoint management
Productivity tools
Document Management
End-User Computing Applications
End-User Computing Strategy
Mobile
Voice & Video Management
Continuous Integration
Technical Product Management
DevOps
Continuous Deployment
Development
Quality Assurance
Customer Relationship Management
Enterprise Content Management
Customer Success
Enterprise Information Management
Finance
Enterprise Resource Planning
HR
Legal
Marketing Solutions
Retail
Human Resource Systems
Marketing
Product Recommendation
Sales
Risk Management
GDPR
SOX Compliance
Governance, Risk & Compliance
Infrastructure & Operations
Cloud Strategy
I&O Finance & Budgeting
Operations Management
Network Management
DR and Business Continuity
Server Optimization
Leadership
Attract & Select
Cost & Budget Management
Engage
Culture
Manage Business Relationships
Innovation
Organizational Design
Program & Project Management
Train & Develop
Values
Talent management
Performance Measurement
Organization Structure
Manage & Coach
Availability Management
Financial and Vendor Management
Reporting
Service Desk
Management Tools
Enterprise Service Management
People & Process
Process Management
Asset Management
Project & Portfolio Management
Portfolio Management
Project Management Office
Pulse
Security
Confidentiality, Integrity, Availability
Secure Cloud & Network Architecture
Endpoint Security
Data Privacy
Identity and Access Management
Security Operations Center
Security Strategy & Budgeting
Security Vendor Landscapes
Threat Intelligence & Incident Response
Threat & Vulnerability Management
Vendor Management
Infrastructure Vendor Landscapes
Budgeting
Roadmap
Outsourcing
Strategy & Operating Model
Business Continuity
Architecture Domains
Strategy
Tool Recommendation
What are some examples of business value creation through AI/Machine Learning?

Top Answer :

There is no doubt that AI and Machine Learning have had a tremendous impact on business. It fact, it is being applied to almost every industry there is. Here are some quick examples of how AI/ ML have created business value across several industries:

Financial Services (My industry) – The Financial services industry relies heavily on AI/ ML algorithms to help detect fraud in real time, saving companies millions in losses. They also use these algorithms to give their customers (e.g. merchants/ SMBs) access to online business trends and peer benchmarking – adding real value to these businesses every day.

Retail – This is an industry that has been on the forefront of this tech in order to improve customer experiences. They use AI/ML to create a seamless experience between online interactions and in-store purchases. Think about Walmart and their experiments with facial recognition technology to determine if their customers are happy or sad at their point of purchase.

Farming – Yes AI/ ML has also reached farming. John Deere has actually invested in building robots that make decisions to treat plants with pesticide based on immediate visual data – taking weather and infestations into account!

Manufacturing – Auto manufacturers have done a good job with collecting data from cars to predict when parts would fail or when they need servicing. This not only allows them to uphold their safety records but also allows them to create products to improve driver and passenger needs. 

3689 views
3 comments
2 upvotes
Related Tags
Do Quality Assurance testing models fall short when it comes to Artificial Intelligence / Machine Learning software?

Top Answer : It's a human factor. In my mind, you can't have a machine validate a machine because you'll run into the same issue. You need to have that diversified, unbiased view asking questions: is this leaning towards one way, or the other? What if? Why? And that is a human factor. 20 or even 40 years from now the only job that we’ll have is validating whether or not the output of all of these automated systems is just, ethical, correct, or otherwise. It's a very tough question.