Some common ways to use AI
For developing an AI strategy always remember some common ways to use
- Developing more intelligent products
- Developing more intelligent services
- Making business processes smarter
- Automating repetitive business tasks
- Automating manufacturing processes
Startup the AI strategy
- Leverage AI to make a plus specific to your company
- A design strategy that aligns with a virtuous cycle of AI
- Blue River – precision agriculture
- AI must be specialized or verticalized to your industry sector
- Don’t compete with giants Creating a strategy
- Strategic data acquisition
- Unified data warehouse – Pull data into a single repository, the software can
- Create network effect and platform advantages Uber, Careem, Facebook
Low-cost strategy - Low-cost strategy
- High-value strategy
Develop internal and external communications
- Investor relations
- Government relations
- Consumer / user education
- Talent / recruitment
- Internal communication
AI teams
- AI teams may have hundreds of engineers.
- A small team can have four or five members.
- Example role: Software Engineer (Execute joke, set timer)
- Machine learning Engineer
- Machine learning researcher (Extend state-of-the-art, applied Machine learning scientist in-between machine learning researcher and machine learning Engineer)
- Data Scientist (Provide insights, make a presentation to team/executives)
- Data Engineer (Organize data, data is saved in a cost-effective way, we have a lot of data, scalability is important)
- AI Product Manager ( Help decide what to build; what’s feasible and valuable).
Always Keep in Mind
- Data privacy will be a key consideration during adopting the AI strategy.
- Make sure that AI is free of bias and discrimination.
- AI should be used for the benefit of a company, customers, and its employees.
- Never adopt the adverse uses of AI.
Common PitfallsDon’t
- Expect AI to solve everything
- Hire 2-3 ML engineers and count solely on them to return up with use cases.
- Expect the AI project to figure the primary time.
- Expect the traditional planning process to apply without changes.
- Think you would like superstar AI engineers before you’ll do anything.
Do
- Be realistic about what AI can and can not do given limitations of technology, data, and engineering resources.
- Pair engineering talent with business talent and work cross-functionally to seek out feasible and valid.
- Plan for AI development to be an iterative process with multiple attempts needed to succeed.
- Work with the AI team to determine timeline estimates, milestones, KPIs, etc.
Take your first step
- Get friends to learn about AI
- Start brainstorming projects
- Hire a couple of machine learning/data science people to assist.
- Hire/appoint an AI leader (Vice President AI, Chief AI Officer)
- Discuss with CEO about possibilities of AI transformation
Get started with a small team
- One software Engineer
- One machine learning/ Data Scientist
- Nobody but yourself
Building AI projects:
- How to choose an AI project?
- Just keep in mind the AI knowledge and domain knowledge.
- Brainstorming frameworks means group discussion to produce ideas or solve problems.
- Automate tasks rather than job
- Main drivers of business value
- What are the pain points in your business
Is it always necessary to have big data?
- Having more data is good
- With small data sets, we can make progress
- 10,100 or 1000 data points can be a good start
Technical diligence
- Can AI team/system meet desired performance
- How much data is needed
- Engineering timeline
Business diligence
- Lower cost
- Increase revenue
- Launch new product or business
Build Vs Buy
- Machine learning projects can be housed or outsourced.
- Data Science projects are generally in-house.
- Buy industry standard, only build specialized products.
Working with an AI team
- Specify your acceptance criteria (Goal: detect defects with 95% accuracy)
- Training, validation, and test data set
- Don’t expect 100% accuracy (Limitations of machine learning, insufficient data, mislabeled data, ambiguous labels
AI technical Tools
- Machine learning frameworks for writing software ( Tensor flow, Pytorch, Keras, MX Net, CN TK Caffe, Paddle Paddle, SciKit-learn, R, Weka)
- Research publications (Arxiv)
- Open Source Repositories (Git hub)
