How to get started with AI – before it’s too late

These days, Artificial Intelligence is becoming more popular because of capability and influence at an astonishing rate across virtually every industry. Yet the speed and scale of AI is such that some long-standing norms are facing disruption. In this article, we would show you 5 ways  to explore what each of us can do to prepare for this bold new future and make the most of AI.

1. Make yourself  an AI expert 

Recently, many people believe that it will take too long to learn it yourself. But these are early days. Thanks to the development of the Internet, AI becomes the biggest opportunity; it’s just getting started. Relying on your computer science and math expertise, you’ll want to brush up on the following:

1. Statistics
2. Calculus
3. Linear algebra
4. Algorithms
5. Convex optimization
6. Graph theory
7. Current programming tools and trends
8. Data analysis

To begin with, you should grasp practical skills helping you understand machine learning at a low level. Here they are:

1. Data wrangling
2. Cross validation
3. Distributed computing
4. Data visualization
5. Database management
6. Feature engineering

In addition, if you are processing your big data set to choose features and explore its statistical properties, R would be a great tool to familiarize yourself with. An ideal way to get up to speed on these subjects are Data science bootcamps like The Data Incubator and Zipfian Academy. Also, it’s noticeable that the exact role and necessary skill set of a data scientist varies depended on the problem they’re attempting to solve.

2. Employ an AI expert

As the ‘sexiest job of the 21st century’, the demand for data scientists may be 60% greater than supply by 2018 in job market. Data scientists are the combination of  programming, math expertise and analytic skills, which makes them so attractive.

The data scientists are typically employed by large research universities or large tech companies like Google and Facebook. Although the prospect of building a self-driving car is likely more rewarding than creating an AI model to help a small company automate insurance forms, the use cases for AI are getting more intriguing. In addition, if you run a cool project, you may merely convince one of these mythical data scientist unicorns to join your team. You can buy up a university robotics department if you’ve got millions in the bank.

3. Open-source libraries and frameworks

In 2006, machine learning frameworks made outstanding progress. Over just the last  five months, Microsoft, Baidu and Amazon have all open-sourced their own ML libraries (CNTK, WarpCTC, and DSSTNE, respectively), OpenAI released OpenAI Gym and Google has continued to push major updates to Tensorflow.

Only by stitching together Tensorflow’ s ‘building blocks’, engineers can code an efficient neural network without too much time waste and deep math knowledge, and importantly declining rooms for error. ML libraries- a great reservoir for machine learning practitioners, provide engineers with much control over a model’s outcome as well as the ability to tweak and enhance upon it.

4. Statistical analysis tools

Many companies of all sizes have troubles with mountains of user data. If you have data with no the patterns, statistical analysis tools are a great resource to make your data work harder for you.

You can merely find a few services and tools taking varied approaches to this problem. A service like BigML or DataRobot can take all of your data, try different machine learning models, and choose the ideal fit to your specific business problem.

To find patterns in noisy data sets and to gain more information from your own data, these back boxes would be a great tool with no machine learning background required. If you’re not satisfied with the model, you’ll have to go back to the service and generate a new model.

5. APIs

Using API could be the fastest method to apply AI technology into your business. IBM and Google, alongside many new AI startups, have released APIs related to natural language processing, visual recognition, and semantic analysis.

It’s noticeable that each API has a mission to do just one certain thing. Take Google’s Cloud Vision API for example. Boasts of its ability to help applications see does very well at that for certain tasks. You would need to build your own neural network and train it on images of that anomaly so it can draw a lesson about how to identify it.

Currently, APIs are likely to determine emotions of tweets, translate languages, translate text to audio, recognize the emotion of human faces and analyze data. It’s a great way to boost the intelligence and productivity of your app fast if your problem falls into the wheelhouse of one of these APIs.

If you put these ways together, the rest should follow as you transition from the Information Age to the Insight Age.