Artificial intelligence is everywhere. Siri, Alexia, Netflix, Amazon Prime, and chatbots are all powered by artificial intelligence. AI offers a smooth experience to all consumers.
- As per Servion Global Solutions, 95% of all customer interactions will be powered by AI by 2025.
- According to Gartner, more than 33% of technology providers are planning to invest upwards of $1 million in AI between 2022 and 2023.
- The global AI market will reach $62 billion in 2022 according to Gartner
- A cross-platform report by Nielsen shows that consumers spend 89% of their time on mobile apps and only 11% browsing the internet.
The big players make the integration of AI into applications look complex and costly. However, there are a few easy-to-use AI tools that can equip your app with natural language processing. That can enable your applications to
- Respond to consumer queries
- Offer suggestions to consumers based on search history
- Recommend products/services.
To make matters easier for every app owner and developer, 2022 has brought scores of AI tools. Here are the top 8 picks every android developer should know –
1. TensorFlow
TensorFlow is an open-source software library for AI and ML. TensorFlow utilizes an array of multi-layered hubs, which focus on the training of deep neural networks. These enable the developers to set up, train and send duplicate neural systems with big databases in no time.
TensorFlow allows developers to compose a program on Python and keep running it on their GPU or CPU. So, the users don’t have to compose at a C++ or CUDA level. Developers can use pre-trained models to train their own models.
Using TensorFlow the hired android developers can prepare necessary data, build ML models, implement the MLOps and deploy the models. It is a platform that allows the creation of models and the productionization of ML.
TensorFlow is the tool that allows Google to recognize questions in photos and understand verbally expressed queries in voice-search options.
2. Theano – PyPI
Theano is an open-source Python library. It allows developers to optimize, define and evaluate mathematical expressions that involve multi-dimensional arrays. Theano enables the users to run the computations efficiently on GPU and CPU architectures.
Its interface is similar to NumPy. If the Android developers you hire have experience with NumPy, they should be able to work seamlessly with Theano. Additionally, it supports dynamic C code generation.
It is neatly folded over Keras – a neural systems library. Keras runs in tandem with Theano. Developers can use Theano to create profound learning models quickly and simply. It can utilize the GPU in addition to the CPU, which makes it swift. Theano’s incredible speed is ideal for profound learning and other complex computational tasks.
3. MxNet
Apache MXNet is an open-source deep learning software framework. It can train and deploy deep neural networks. It supports flexible programming and 8 languages including C++, Java, Python, R, JavaScript, Julia, Scala, and Perl.
MxNet is scalable and it enables super-fast model training. It enables the trading of computation time for memory. It is a unique way to speed up computation time during recurrent nets on long sequences.
It features a thriving set of tools and libraries such as D2L.ai, GluonCV, GluonTS, and GluonNLP. These enable use cases in NLP, computer vision, and time series.
Hire android developers with experience in using AI tools like MxNet. This particular tool can help your developer team to define, train and deploy deep neural networks on multiple devices. You can join the Apache MxNet community to find answers to your queries and solutions to your problems.
4. Keras
Keras is an open-source software library. It is for all the developers comfortable working with Python. Keras provides a user-friendly, Python interface for developing advanced artificial neural networks. Its main aim is to reduce the cognitive load of the developer so that they can focus on other important tasks.
Keras is an interface for the TensorFlow library. It is compatible with several backends including Microsoft Cognitive Toolkit, TensorFlow, and Theano. It comes with industry-level performance and scalability.
Android developers can use Keras for creating deep models that can be compatible with multiple makes and models of smartphones. Developers can also use it for distributed training of these deep learning models.
Several popular now use Keras, such as Yelp, NASA, Uber, and Netflix.
5. Dialogflow
Android developers can use Dialogflow to integrate conversational user interfaces into mobile applications. It is a natural language understanding platform.
Dialogflow allows the android developers you hire to build interactive interfaces. Such interfaces may include chatbots and conversational IVR. The users can experience rich interactions with your app without any human intervention from your side.
The Dialogflow Enterprise Edition gives the developers access to Google Cloud Support and SLA for product deployment.
6. H20: Open Source AI Platform
The H2O AI cloud is an open-source AI cloud platform. It is a machine learning platform any android developer can utilize. It is a distributed, fast, and linearly scalable platform. It strengthens ML and statistical algorithms that are integral parts of deep learning and generalized linear models.
The platform’s interactive graphical user interface does not require any coding. It is an ideal choice for all android developers comfortable with Python, Scala, Java, and R.
Android developers can leverage the H2O AI platform for deep learning. The platform boasts superior algorithms such as Word2Vec, Random Forest, XGBoost, and gradient boosting (GBM). The H2O AI GUI is compatible with multiple browsers including Chrome.
7. Caffe
Caffe is an open-source deep learning framework that was originally developed at the University of California. It has a Python interface, which makes it easier for Android developers to use Caffe. GitHub currently hosts Caffe.
It supports CNN, RCNN, and fully connected neural network designs. It supports several types of deep learning architectures that allow image classification and image segmentation. Today, Caffe supports CPU- and GPU- based acceleration computational kernel libraries such as Intel MKL and NVIDIA cuDNN.
Currently, Caffe is most popular in academic research projects, large-scale design applications (in multimedia), and startup prototypes. Yahoo! has integrated Apache Spark and Caffe to create CaffeOnSpark. It is a distributed deep learning framework.
8. PyTorch 1.0
PyTorch is an open-source machine learning (ML) framework. It is based on Python and the Torch library. Developers prefer it for deep learning research. The framework ideally speeds up the process between the research prototyping phase and deployment.
Facebook announced the launch of Caffe2 in April 2017. Caffe2 is an evolved version of Caffe since it features Recurrent Neural Networks.
It is a lightweight, scalable and modular deep learning framework. It’s the next-gen tool for android developers looking to create advanced mobile and hybrid applications.
Developers commonly use Caffe2 for production purposes. It is suitable for applications involving large-scale image classification and the detection of objects. Caffe2 mainly focuses on scalable systems and cross-platform support.
Right now, Caffe2 is a part of PyTorch. The two have joined together to create PyTorch 1.0
Wrapping it Up
These are the 8 AI tools that every Android developer needs to know for creating advanced and intuitive applications. These AI tools hasten the coding, designing of the UI/UX, and deployment processes. Before hiring android developers ask them if they are comfortable using these tools if your android app needs neural language processing, big data management, predictive analytics, and deep learning.
