Machine Learning Professional Services:

Adopting AI is a process the involves these 4 steps:

1. Understanding AI

AI is the theory and development of computer systems able to perform tasks that normally require human intelligence.

These are key concepts:

  • • Machine learning:

    Machine learning covers a range of statistical techniques giving computers the ability to learn. That is, they progressively improve their capacity to execute a task.

  • • Deep learning using neural networks:

    A neural network is a type of machine learning which models itself after the human brain, creating an artificial neural network that via an algorithm allows the computer to learn by incorporating new data. While there are plenty of artificial intelligence algorithms these days, neural networks are able to perform what has been termed deep learning. While the basic unit of the brain is the neuron, the essential building block of an artificial neural network is a perceptron which accomplishes simple signal processing, and these are then connected into a large mesh network. The computer with the neural network is taught to do a task by having it analyze training examples, which have been previously labeled in advance. A common example of a task for a neural network using deep learning is an object recognition task, where the neural network is presented with a large number of objects of a certain type, such as a cat, or a street sign, and the computer, by analyzing the recurring patterns in the presented images, learns to categorize new images.

  • • RPA:

    The process of automating business operations with the help of robots to reduce human intervention is said to be Robotic Process Automation(RPA).

  • • Natural Language Processing:

    Natural Language Processing, or NLP, focuses on interactions between computers and human languages. NLP is a field that brings together computer science, artificial intelligence, and linguistics. Computers are great at handling structured data such as database tables and spreadsheets. But human language is incredibly diverse and complex, and often far from tightly-structured. Human language spans across hundreds of languages and dialects, with large sets of grammar rules, syntaxes, terms, and slang. Homonyms, or words that are spelled and sound the same but carry different meanings, create an interesting NLP problem. "Paris Hilton listens to Paris Hilton at the Paris Hilton" is a sentence that native English speakers don't have too much trouble parsing but creates a complicated NLP problem. When does "Paris" refer to a person, and when does it signify a hotel's location in France? Natural Language Processing allows computers to communicate with humans in their own language by pulling meaningful data from loosely-structured text or speech. NLP helps scale language-related tasks. This is what makes it possible for computers to read text (or hear speech), interpret that text or speech, and determine what to do with the information. NLP helps to resolve ambiguity in language by adding numeric structure to large datasets. This structure makes speech recognition and text analytics possible. The field of NLP has grown rapidly in the last decade. Thanks to advancements in the field of natural language processing and technologies built on it, someone can now say to a device in their home, "Hey Google, play Never Gonna Give You Up" and hear their favorite song played back to them.

  • • Computer vision:

    Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do.

  • • Speech Recognition:

    Speech Recognition can be defined as the ability of a machine or program to identify words or phrases in spoken language to a machine readable format. It is the process of converting a speech signal into word sequence.

  • • Expert System:

    An Expert System is defined as an interactive and reliable computer-based decision-making system which uses both facts and heuristics to solve complex decision-making problems. It is considered at the highest level of human intelligence and expertise. It is a computer application which solves the most complex issues in a specific domain. The expert system can resolve many issues which generally would require a human expert. It is based on knowledge acquired from an expert. It is also capable of expressing and reasoning about some domain of knowledge. Expert systems were the predecessor of the current day artificial intelligence, deep learning and machine learning systems.

2. Building a case for AI

By deploying the right AI technology, your business may gain an ability to:
  • • save time and money by automating and optimising routine processes and tasks.

  • • increase productivity and operational efficiencies.

  • • make faster business decisions based on outputs from cognitive technologies.

  • • avoid mistakes and 'human error', provided that AI systems are set up properly.

  • • use insight to predict customer preferences and offer them better, personalised experience.

  • • mine vast amount of data to generate quality leads and grow your customer base.

  • • increase revenue by identifying and maximising sales opportunities.

  • • grow expertise by enabling analysis and offering intelligent advice and support.

  • According to a recent study, the main driving force for using AI in business are:
    •      • competitor advantage

    •      • an executive-led decision

    •      • a particular business, operational or technical problem

    •      • an internal experiment
    •      • customer demand

    •      • an unexpected solution to a problem

    •      • an offshoot of another project

3. From theory to AI strategy

  • 1. Discover AI use cases.

  • 2. Analyze AI use cases and capabilities for impact, effort, and risk By gathering your metrics: .

    • • Time to Fill

    • • Candidate Conversion Rates (in each process step)

    • • Hiring Manager Satisfaction

    • • New Hire Productivity

    • • New Hire Performance/Promotability

    • • Average Turnover

    • • Candidate Experience

    • • New Hire Diversity

    • • Cost to Hire

    • • Hiring Manager/Recruiter Productivity

    • • Cost of Vacancy

  • 3. Prioritize AI use cases and capabilities, given dependencies and complements in the business plan.

  • 4. Translate Conventional Metrics into ROI Metrics.

4. Putting AI strategy into practice

  • Proof of concept (POC):

    validate the concept that is supposed to be developed, check whether a chosen software development is capable of creating it, and explore different solutions that could essentially bring the idea into life.

  • • Production pilot:

    take the POC into production with a defined, narrow scope to test the solution in the real environment and validate the business case.

  • • Scale up:

    identify roadmap and expansion options and integrate the system more tightly with your current operations.