Artificial intelligence: Cheat sheet

Learn the basics of artificial intelligence, business use cases and more in this beginner's guide to the use of AI in business.

Artificial intelligence (AI) is the next big news in business computing. Its uses come in many forms, from simple tools that respond to customer chat, to complex machine learning systems that predict the trajectory of an entire organization. Popularity does not necessarily lead to familiarity, and despite its constant appearance as an avant-garde feature, AI is often misunderstood.

To help business leaders understand what AI is capable of, how it can be used and where to start an AI journey, it is essential to first dispel the myths surrounding this great leap in computer technology. Get more information on this AI cheat sheet.

SEE: All TechRepublic cheat sheets and smart people guides

What is artificial intelligence?

When artificial intelligence comes to mind, it's easy to enter a world of science fiction robots like Data from Star Trek: The Next Generation Skynet Series Terminator and Marvin, the paranoid android of The Hitchhiker's Guide to the Galaxy .

However, the reality of AI is nothing like fiction. Instead of totally autonomous thinking machines that mimic human intelligence, we live in a time when computers can be taught to perform limited tasks that involve making judgments similar to those of people, but they are far from being able to reason Like human beings.

Modern AI can perform image recognition, understand the natural language and writing patterns of humans, make connections between different types of data, identify pattern abnormalities, develop strategies, predict and more.

All artificial intelligence boils down to a central concept: pattern recognition. The core of all AI applications and varieties is the simple ability to identify patterns and make inferences based on those patterns.

SEE: Artificial intelligence: a guide for business leaders (free PDF) (TechRepublic)

Artificial intelligence is not really intelligent in the way we define intelligence : can & # 39; He does not think and lacks reasoning skills, does not show preferences or have opinions, and cannot do anything outside the very limited scope of his training.

That does not mean that AI is not useful for companies and consumers trying to solve real-world problems, it just means that we are not near machines that can really make independent decisions or reach conclusions without being given Data. appropriate first. Artificial intelligence remains a marvel of technology, but it is still far from replicating human intelligence or truly intelligent behavior.

What can artificial intelligence do?

The power of AI lies in its ability to become an incredible ability to do things in which humans train it. Microsoft and Alibaba independently built AI machines capable of better understanding reading than humans, Microsoft has an AI that is better in speech recognition than its human builders, and some researchers predict that AI will outperform humans in almost everything In less than 50 years. [19659003] That does not mean that those AI creations are truly intelligent, only that they are capable of performing tasks similar to those of humans more efficiently than we, the error-prone organic beings. If I had to try, for example, to give a voice recognition AI an image recognition task, it would fail completely. All artificial intelligence systems are designed for very specific tasks and do not have the ability to do anything else.

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What are the commercial applications of artificial intelligence?

Modern AI systems are capable of doing amazing things, and it is not difficult to imagine what kind of business tasks and problem-solving exercises could be adapted. Think of any routine task, even incredibly complicated tasks, and there is a possibility that an AI can do it more accurately and quickly than a human, just don't expect it to make reasoning at the science fiction level.

In the business world, there are many applications of artificial intelligence, but perhaps none is gaining as much strength as business analytics and its ultimate goal: prescriptive analytics.

Business analytics is a complicated set of processes that aim to model the current state of a company, predict where it will go if it stays on its current trajectory, and model potential futures with a given set of changes. Before the era of AI, analytical work was slow, cumbersome and limited in scope.

SEE: Special report: Management of AI and ML in the company (ZDNet) | Download the free PDF version (TechRepublic)

When modeling a company's past, it is necessary to take into account almost infinite variables, classify tons of data and include them all in an analysis that builds A complete picture of the current state of an organization. Think about the business you are in and all the things that should be considered, and then imagine a human trying to calculate everything, cumbersome, to say the least.

Predicting the future with an established model of the past can be quite easy, but prescriptive analysis, which aims to find the best possible outcome by adjusting the current course of an organization, can be completely impossible without the help of AI.

SEE: Artificial intelligence ethics policy (TechRepublic Premium)

There are many artificial intelligence software platforms and AI machines designed to do all that heavy work, and the results are companies in transformation: what once was beyond the reach of smaller organizations is now feasible, and companies of all sizes can make the most of each resource by using artificial intelligence to design the perfect future.

Analytics may be the rising star of commercial AI, but it is not the only application of artificial intelligence in the commercial and industrial world. Other cases of AI use for companies include the following.

  • Recruitment and employment: Human beings can often overlook qualified candidates, or candidates may fail to be noticed. Artificial intelligence can expedite recruitment by filtering more candidates more quickly and by noticing qualified people who may go unnoticed.
  • Fraud detection: Artificial intelligence is excellent for detecting subtle differences and irregular behavior. If they are trained to monitor financial and banking traffic, artificial intelligence systems can detect subtle indicators of fraud that humans can ignore.
  • Cybersecurity : As with financial irregularities, artificial intelligence is excellent for detecting indicators of piracy and other cybersecurity problems.
  • Data management: Use of AI to categorize raw data and find relationships between elements that were previously unknown.
  • Customer Relations: Modern chatbots with AI technology are incredibly good at holding conversations thanks to natural language processing. Chatbots with AI can be an excellent first line of customer interaction.
  • Medical care: Not only some AIs can detect cancer and other health problems before doctors, but they can also provide comments on patient care based on long records and long trends term.
  • Prediction of market trends: Like prescriptive analysis in the world of business analysis, artificial intelligence systems can be trained to predict trends in larger markets, which can Take companies to move forward. emerging trends
  • Reduction of energy use: Artificial intelligence can expedite the use of energy in buildings and even in cities, as well as make better predictions for construction planning, oil extraction and gas, and others focused on energy. Projects.
  • Marketing: AI systems can be trained to increase the value of marketing for both individuals and larger markets, helping organizations save money and get better marketing results. [19659045] If a problem involves data, there is a good chance that AI can help. This list is barely complete, and new innovations are being made in AI and machine learning all the time.

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    What AI platforms are available?

    When adopting an AI strategy, it is important to know what types of software are available for business-focused AI. There are a wide variety of platforms available from the usual cloud hosting suspects like Google, AWS, Microsoft and IBM, and choosing the right one can mean the difference between success and failure.

    AWS Machine Learning offers a wide variety of tools that run in the AWS cloud. Artificial intelligence services, preconfigured frames, analysis tools and more are available, and many of them are designed to facilitate initial work. AWS offers pre-built algorithms, one-click machine learning training and training tools for developers to start or expand their knowledge about AI development.

    Google Cloud offers AI solutions similar to AWS, as well as having several pre-built total AI solutions that organizations can (ideally) connect to their organizations with minimal effort.

    The Microsoft AI platform comes with pre-generated services, ready-to-deploy cloud infrastructure and a variety of additional AI tools that can be connected to existing models. Its artificial intelligence laboratory also offers a wide range of artificial intelligence applications that developers can play with and learn from what others have done. Microsoft also offers an AI school with educational clues specifically for commercial applications.

    Watson is the IBM version of cloud-based machine learning and enterprise AI, but it goes a little further with more AI options. IBM offers customized on-site servers for artificial intelligence tasks for companies that do not want to rely on cloud hosting, and also has IBM AI OpenScale, an artificial intelligence platform that can be integrated into other cloud hosting services, which It could help avoid vendor blocking.

    Before choosing an AI platform, it is important to determine what kind of skills you have available within your organization and what skills you will want to focus on when hiring new members of the AI ​​team. Platforms may require specialization in different types of development and data science skills, so be sure to plan accordingly.

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    What AI skills will companies need to invest in?

    With commercial AI taking so many forms, it can be difficult to determine what skills an organization needs to implement it.

    As previously reported by TechRepublic, finding employees with the right set of artificial intelligence skills is the problem most commonly cited by organizations seeking to start with artificial intelligence.

    The skills required for an AI project differ according to the business needs and the platform used, although most of the larger platforms (such as those mentioned above) support most, if not all, of the programming languages and most commonly used skills needed for AI.

    SEE: Don't miss our latest coverage on AI (TechRepublic on Flipboard)

    TechRepublic covered in March 2018 the 10 most demanded AI skills, which is a Excellent summary of the types of training an organization should consider when building or expanding an artificial business intelligence team:

    1. Machine learning
    2. Python
    3. R
    4. Data science
    5. Hadoop
    6. Big data
    7. Java
    8. Data mining
    9. Spark
    10. SAS

    Many platforms Business AIs offer training courses on the specific aspects of the operation of their architecture and the programming languages ​​needed to develop more AI tools. Companies that take AI seriously must plan to hire new employees or give existing ones the time and resources needed to train in the skills necessary for AI projects to succeed.

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    How can companies start using artificial intelligence?

    Starting with artificial business intelligence is not as easy as simply spending money on an artificial intelligence platform provider and starting up some predefined models and algorithms. It takes a lot to successfully add artificial intelligence to an organization.

    At the heart of all this is good project planning. Adding artificial intelligence to a company, no matter how it will be used, is like any business transformation initiative. Here is a summary of just one way to address how to start with commercial AI.

    1. Determine your AI target . Discover how AI can be used in your organization and for what purpose. By focusing on a narrower implementation with a specific objective, you can better allocate resources.

    2. Identify what must happen to get there . Once you know where you want to be, you can find out where you are and how to make the trip. This could include starting to classify existing data, gather new data, hire talents and other steps prior to the project.

    3. Build a team. With a final goal in sight and a plan to get there, it's time to gather the best team to make this happen. This may include current employees, but don't be afraid to leave the organization to find the most qualified people. Also, be sure to allow existing staff to be trained so that they have the opportunity to contribute to the project.

    4. Choose an AI platform. Some AI platforms may be more suitable for particular projects, but in general all offer similar products to compete with each other. Let your team give you recommendations on which AI platform to choose: they are the experts who will be in the trenches.

    5. Implementation begins . With a goal, a team and a platform, you are ready to start working seriously. This will not be fast: AI machines must be trained, tests must be performed on subsets of data and many adjustments must be made before a business AI is ready to reach the real world.

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