The next wave of AI tools are self-learning
Training your AI systems efficiently will require a large dataset.
When thinking of powerful AI it is critical to initially concentrate on the data collection required to drive the Machine Learning and Natural Language Processing.
The more data you can collect from the user and send to a well-structured database, the more information your Natural Language Processing and Machine Learning will have to create a world-class AI experience.
Having a big dataset isn’t the only thing that matters, it also needs to be curated. Having random incorrect data won’t make your AI smarter, it will make it less intelligent or wrong.
AI data scientists spend significant amounts of time making sure you have a clean set of sentences to train your conversational systems.
A data-centric approach can keep your idea from becoming antiquated
Linking AI capabilities to a specific platform or technology will lock you into a specific ecosystem like Alexa, Cortana, or whatever platform you used to build your AI system.
Your goal should always be to remain platform independent, so you can flexibly deploy on any AI platform using the same core data.
If you focus on your data and the Natural Language Processing and Machine Learning that processes it on the back-end, you will be able to easily and rapidly switch any UI your strategy dictates.
AI that trains itself is a game-changer
Any AI or Machine Learning data scientist will tell you that training these technologies is a pain. Powerful AIs all have one thing in common, they require extensive training based on carefully curated datasets.
One emerging technology that trains itself is pattern recognition. Pattern recognition trains itself over time as it processes more examples.AI experts can spend much less time on repetitive tasks and has the benefit of making these expensive and difficult to find experts more efficient.
Automating the training is a game-changer
Automating the training of machine-learning systems will make AI more accessible.
Many companies such as AirBNB, Uber, BlablaCar have based their business on people working together. The same can be applied to AI learning.
For example, Recast.AI built a collaborative bot platform where all their users participate in the global training of their models for their bots.
This approach makes your AI smarter every day because the training is shared across the user-base which drastically increase bot time-to-market.
The cloud is critical
The biggest challenge developing AI systems is recruiting AI talent, and it means that only big companies with deep pockets can usually afford to build their own AI algorithms. Cloud computing is one of the keys to making AI more accessible.
Google, Amazon, Microsoft, and other companies are rushing to add machine-learning capabilities to their cloud platforms.
Google Cloud already offers many such tools, but they use pretrained models. That limits what they can do—for example, programmers will only be able to use the tools to recognize a limited range of objects or scenes that they have already been trained to recognize.
A new generation of cloud-based machine-learning tools that can train themselves will make the technology far more versatile and easier to use.
Google’s self-training AI turns existing coders into machine learning specialists
Google has made it a lot easier to build a custom AI system.
Their new service, called Cloud AutoML, uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images.
The technology is limited for now, but this marks the start of something big.
Building and optimizing a deep neural network algorithm typically requires a detailed understanding of the underlying math and code, as well as extensive practice tuning the parameters of algorithms to get things right.
Google researchers have been testing the limits of automating AI for some time now.
In 2016, one Google team demonstrated that deep learning could itself be used to identify the best tweaks to a deep-learning system.
In 2017 another group at Google used simulated natural selection to “evolve” an optimal network architecture.
In 2018, two Google scientists used reinforcement learning to automatically improve a deep-learning system.
Efforts in these promising areas will feed the overall effort to build more general and adaptable forms of artificial intelligence that learn themselves, are cheaper, and are more efficient to deploy.