Artificial intelligence is the concept of having machines “think like humans” — to perform tasks like reasoning, planning, learning, and understanding language.
It is the ability to store large volume of data in the cloud and provide easier access to advanced algorithms. AI makes our platforms and systems smart enough to learn from our interactions or data and not only to do what we ask, but also to anticipate our needs, taking care of forgotten tasks and reminding us of important ones.
We are already using AI in our regular browsing, Google harnesses AI to autocomplete search queries, predicting what we are searching for with greater accuracy and without human involvement, Facebook news feed and amazon product recommendations are tailored for us via machine learning algorithms.
The brain behind AI is a technology called machine learning, which is designed to make our jobs easier.
Machine Learning is the core driver of AI and involves computer learning from data with minimal programming. It is a data science technique that allows computers to use existing data to forecast future behaviours, outcomes, and trends.
Natural Language Processing (NLP)
It uses machine learning techniques to find patterns within large data sets to recognise natural language.
E.g. – Patterns in social media post to understand how customer feel about specific brand or product.
Predictive analytics uses math formulas called algorithms that analyse historical or current data to identify patterns or trends to forecast future events.
Importance Of AI
- Easy Modification: AI programs can absorb new modifications by putting highly independent pieces of information together.
- Quick Solutions: AI can answer the generic questions it is meant to solve.
- Dealing with mundane tasks: It can even remove “boring” tasks from humans and free them up to be increasingly creative.
- Faster Decisions: Using artificial intelligence alongside cognitive technologies can help make faster decisions and carry out actions quicker.
- Avoiding Errors: With artificial intelligence, data could be processed error-free, no matter how big the dataset might be.
Now when we have understood what AI is and its importance, let’s know about some of the popular AI platforms.
Microsoft Azure Machine Learning
Microsoft Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.
You can work from a ready-to-use library of algorithms, use them to create models on an internet-connected PC, and deploy your predictive solution quickly. The finished model can then be deployed in minutes as a web service, which can connect to any data, anywhere.
- it provides memory flexibility, and eliminates setup and installation concerns as work is done through web browser.
- it is a cloud managed and fully scalable service that allows you to easily build, deploy and share predictive analytic solutions.
- It features a drag and drop interface and supports any operating system, tool, language, and framework.
- it can also be published out to the community in the product gallery or into the machine learning marketplace.
Google Cloud Prediction API
It provides a restful api to build machine learning models. Prediction’s cloud based machine learning tools can help analyse data to add various features in the applications. After it learns from training data, Prediction api can predict a numeric value or choose a category that describes a new piece of data like categorising emails as spam or non-spam, assessing whether posted comment on social media have positive or negative sentiments, recommendation system and more.
- It can integrate with google app engine, and the restful api is available through libraries.
- Cloud based machine learning.
- Purchase Prediction.
- Spam Detection
- Recommendation System
- Customer Sentiment Analysis
- Smart autofill Spreadsheet
tensor Flow is an open source Library developed by google brain team. It is a universal library specially created for the tasks that require heavy numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional array (tensors) communicated between them.
- Users can write their own high-level libraries on top of tensor flow.
- Flexible architecture: Deploy computations on CPU’s or GPU’s, and on desktop, server, or mobile devices using single api.
- Language options.
- Provides both python and C++ Api.
- Faster Compilation time.
Infosys Nia is a Knowledge-based platform that brings machine learning together with the deep knowledge of an organization, to drive automation and innovation and enabling organizations to amplify their capabilities, and businesses to continuously reinvent their system.
- Infosys Nia AutoModel capability allows a user to build ML models even if they do not have any knowledge about it, or allows the expert to save time that would have been spent in tedious manual model tuning.
- AutoFeaturize-system is further generalised to automatically perform feature engineering on an input dataset.
- Speed & Scalability – it allows users to extract maximum accuracy while using all their data.
- Ease of Use-Through the GUI you can access the full automation and speed & scale capabilities of the system, without having to write any code. For those users who prefer to write their own code, a programmatic interface is available as an SDK using a binding language such as Python.
- The platform effect-The platform is also extensible to include outside open source tools.
API.AI simplify the machine learning by integrating with cross-platform with multilingual support. The ease of using this API makes it high in demand. API.AI is now Dialog flow.
- Integration: API.Ai can fully integrate with several top platforms and some high in demand messaging application like Facebook, Skype, Twilio SMS integration and Cisco Spark.
- Performance: With runtime machine learning capability, its performance improves in real time.
- Build in Conversational Apps: With its build in text and voice based conversational apps, developer need not have to build a conversational apps from scratch.
- Multilingual: It currently supports 14+ languages.
It is an award winning cognitive reasoning platform. If Rainbird is asked a query, it will draw its own inferences, by exploring the available data, and sometimes by asking efficient questions.
Rainbird consist of two main components Rainbird authoring Platform(builder) and Rainbird cognitive Reasoning engine(Runtime).
Rainbird authoring Platform is the interface of developing Knowledge maps. Each knowledge map can also include connections to multiple external data stores.
Rainbird cognitive Reasoning engine is the run-time powerhouse responsible for operating the systems designed in the Rainbird Authoring Platform. It can handle numerous concurrent queries made by users via web assistants, chatbots, apps, or other worker applications.
- Visual User Interface: it enables experts to add concepts, relationships, facts and rules to see how knowledge map take shape.
- RB Lang: It is Rainbird’s underlying knowledge representation language. It includes a full range of mathematical operators, and easy connectors to external data sources and third-party APIs. RBLang also makes it easy to import structured knowledge from other formats.
- Flexible Rule Engine: When solving queries, Rainbird joins the dots in knowledge map to deliver solutions. This efficiently create exceptionally meaningful models that cope well with uncertainty and missing data.
- Evidence Tree: Rainbird records and can articulate how every individual recommendation was made, with its certainty and every factor that went into making that decision.
- Open Architecture: Knowledge Maps can be easily connected to external data sources or to any third-party API. What you build in Rainbird can be published as an API for easy integration into other software.
- ‘One Click’ Publishing: Quickly publish a chatbot and can embed a web agent in any website using a single line of code.
It is the world’s largest litigation system. It mines the data to find out which Attorneys win before which Judges. It gives Counsel’s prior win rate before your judge.
- Know the track record of your Attorney.
- Select Co-Counsel who have never lost in front of certain Judges.
- Analyse the Court, Judge and Opposing Counsel by their Win Rates and Results.
- Rank Arbitrators based on past decisions and their prior track record as Attorneys.
- Manage hiring, timesheets and invoicing.
- Increased Win Rates.
It is the first comprehensive AI for CRM, designed to help every business to be smarter and more predictive about their customers. Einstein is powered by machine learning, deep learning, predictive analytics, natural language processing and data mining.
To know more about Salesforce Einstein Vision API please refer to our previous blog.
To conclude, these are some of the common platforms available in the ecosystem as of today and I hope this will give you a quick start towards your artificial intelligence goals and learning path.
This is just the beginning of AI and with it’s real life applications and utility it is going to expand beyond imagination in the times to come.