In the 1950s, AI has already begun to appear in humans’ lives. But achieving an artificially intelligent machine wasn’t so simple. After several reports criticizing progress in AI, government funding and interest in the field dropped off, which lead to a period from 1974–80 that became known as the “AI winter.” The field experienced another major winter from 1987 to 1993, coinciding with the collapse of the market for some of the early general-purpose computers and reduced government funding. Since 2006, neural networks and deep learning have emerged, which has made AI enter a fast-growth period.
According to the Business Broadway survey report 2019, Python has become a much-needed programming language to execute data science and AI tasks. Every 3 out of 4 data professionals recommended that aspiring data analysts firstly learn Python to turn data insights into a reporting format (Kristy Hill, 2020).
There are two reasons for Python is the most mainstream programming language utilized for AI. The one important reason is how easy it is to use, especially for people with a non-cs but high math background like AI researchers and data scientists. Another primary reason is that it has an extraordinary selection of libraries.
The most famous libraries we can use for Machine Learning and AI are: (Priyanka Srivastava)
- Pandas for elevated level information structures and investigation. It permits combining and sifting of information, just as social affairs do from other outside sources like Excel, for example.
- Keras is a wrapper around Tensorflow, Pytorch etc. Quick prototyping would be the only benefit the others are due to the underlying framework TF/pytorch.
- TensorFlow for working with profound learning by setting up, preparing, and using artificial neural systems with large datasets.
- Matplotlib for making 2D plots, histograms, graphs, and different types of representation.
- NLTK for working with computational etymology, universal language acknowledgment, and handling.
- Scikit-picture for picture handling.
- PyBrain for neural systems, solo and support learning.
- Caffe for profound discovery that permits exchanging between the CPU and the GPU and handling 60+ mln pictures a day utilizing a solitary NVIDIA K40 GPU.
- Stats models for measurable calculations and information investigation.
- The PyPI storehouse, which offers a diversity of Python libraries.
Facebook and Google run two of the most popular Python based AI libraries (PyTorch and TensorFlow).
In the AI era, Python will be mainly used in the following scenarios:
- Computer vision: Through specific picture mode training, computers understand objects and image contents. This includes areas such as image recognition, target recognition and tracking. For example, face recognition is a typical field of image recognition, which is widely used in corporate employee attendance, store customer recognition, airport and other public domain anti-terrorism recognition.
Python libraries: OpenFace,Scikit-Image, etc.
2. Speech Recognition: Here the computer recognizes and converts human natural language into text. It is widely used in various fields such as home appliances, communications, automotive electronics, medical treatment, home services, consumer electronics, etc.
Python Libraries: TensorFlow, PyTorch, CMU Sphinx, etc.
3. Natural language understanding: Natural language understanding is a general term for a type of task, not a single task. It aims to allow computers to understand the surface and deep meanings expressed by human language. Current application scenarios include automatic question answering systems, machine translation, information retrieval and filtering, information extraction, etc.
Python libraries: Nature Language Toolkit, Gensim, TextBlob, etc.
4. Personalized recommendation: Personalized recommendation is a relatively mature field, but based on deep learning and neural networks, a large number of complex, abstract feature data preprocessing can be simplified to the greatest extent, and even a large number of features can be simply processed Directly thrown into the model can get the desired effect.
Python libraries: content-based, collaborative filtering
5. Games and sports: In this field, many technology companies use trained AI to play against humans. As early as the 1990s, IBM’”Dark Blue” developed by IBM and Kasparov’s century war has caused a sensation in the world; in 2017, AlphaGo defeated the number 1 ranked world Go champion Ke Jie, which once again let the world feel the power of AI.
Python libraries: Pygame, Pyglet, PyOpenGL, etc.
There are many more AI/Python libraries, which underscores the programming language’s importance in developing artificially intelligent machines.
