Release initially on November 9, 2015, Google’s open source platform for Artificial Intelligence, Machine Learning and Deep Learning called TensorFlow, has had a huge impact on AI research. TensorFlow has helped researchers predict cancer, ease machine translation, enhance image recognition, and speed up speech recognition. In February this year, Google announced that they were “excited to see people using TensorFlow in over 6000 open-source repositories online.“ Today, major companies like Uber, Twitter, Snapchat, Qualcomm, Airbnb, Ebay and several others implement TensorFlow.

The Launch of TensorFlow Lite

Unfortunately, TensorFlow was primarily for computers, until now. In Google’s I/O, Dave Burke, the vice president of engineering for Android, announced the soon-to-be-released TensorFlow Lite for mobile. He said “TensorFlow Lite will leverage a new neural network API to tap into silicate specific accelerators, and over time we expect to see DSPs (Digital Signal Processors) specifically designed for neural network inference and training.” He further added “We think these new capabilities will help power the next generation of on-device speech processing, visual search, augmented reality, and more.”

This new library will allow developers to build Machine Learning apps which run faster while being less expensive on the system. The framework will enable development of deep learning models, and thus bring AI with a greater impact to the mobile platform. We could expect social media apps to become smarter by giving better recommendations. Photo, video or document editing apps could soon have cooler tools, enabling higher customizability and enhancing ease of use. New games could have better Augmented Reality graphics and smoother gameplay. Speech recognition and translation apps would become more user friendly, and way smarter. Most importantly, TensorFlow Lite will enable smaller companies to develop high quality AI apps, thus giving them an opportunity to grow significantly.

Any AI software requires training a huge dataset, which remains a major task, and a task which is computationally too expensive to be handled by smartphones. So in spite of TensorFlow Lite, the training of the models would still have to be performed on the cloud. But as Burke mentioned, Android O will introduce “a new framework” to hardware to enable accelerated neural computations. We can be sure that the future will see several modifications not only in software, but in hardware too. Thus, powered with these new technologies, we can push AI to new limits.

Artificial Intelligence (AI)

Artificial Intelligence, or simply AI, is the broad umbrella term describing computer systems attempting to mimic human-like intelligence. John McCarthy, who coined the term in 1956, defines it as “the science and engineering of making intelligent machines.” Today, the areas of AI technology are primarily Robotics, Machine Learning, Machine Vision and Natural Language Processing. But the key benefit of AI in business is Predictive Analytics. Using AI algorithms, enterprises can grow exponentially to gain a significant competitive advantage over their peers. Venture Scanner reveals the exponential growth of funding in AI startups, with 2016 seeing over $ 2.5 Billion.

Machine Learning (ML)

The subfield of AI called Machine Learning (ML) focuses on developing algorithms that can help computer systems learn automatically, without being explicitly programmed. To accomplish this task, a wide range of algorithms have been developed such as Linear Regression, Logistic Regression, Support Vector Machines (SVM), K-Means, Decision Trees, Random Forests, Naive Bayes, PCA and lastly, Artificial Neural Networks (ANN). Venture Scanner reveals that over $ 3.5 billion has been invested in ML applications with over 400 companies investing in the field.

Deep Learning (DL)

But today, the new buzzword ruling the market is Deep Learning (DL), and this technique was born out of ANNs. Not only is it insanely popular, but it is slowly wiping out all other techniques of ML. Deep Learning uses multi-layered neural nets and learns by crunching a large amount of data. Though the core idea was presented in the ’60s, it is only today with the availability of data and powerful Graphical Processing Units (GPUs) that it proved successful. The recent accomplishments of DL were in the field of Machine Vision, Machine Translation, Speech Recognition, Automated Game Playing and Self Driving Vehicles.

20 Best Platforms for AI

Currently, instead of selling their AI software directly, the tech giants are developing platforms to implement Machine Learning and Deep Learning, which will allow customers to build mobile applications on them. And therefore, even smaller companies can develop state-of-the-art software using AI without getting lost in the nitty-gritty of the algorithmic technicalities. Here are 20 of the most popular platforms and APIs:

1. AlchemyAPI
2. Amazon Machine Learning
3. API.ai
4. AT&T Speech
5. BigML
6. Caffe
7. CNTK
8. Deeplearning4j
9. DiffBot
10. DMTK
11. Google Cloud Prediction API
12. IBM Watson
13. Infosys Mana
14. KAI
15. Mahout
16. Microsoft Azure Machine Learning
17. OpenCyc
18. PredictionIO
19. TensorFlow
20. Wit

Today in the world, AI is the biggest game-changer which is causing technology to advance faster than ever before. With the huge availability of data and open-source platforms, it is extremely easy for enterprises to implement AI. Therefore, if you fail to plunge into AI today, you might just find yourself out of business in a few years.