AI Solutions

AI Solutions

What is AI Solutions?

Artificial Intelligence (AI) is a comprehensive classification that includes cutting-edge concepts such as deep learning. In general, AI solutions are all about adapting aspects of intelligence to machines and having them execute tasks that can be natural and straightforward to humans, but extremely complex to program. Moreover, an AI program can perform such tasks autonomously and efficiently.

AI solutions can be sorted into two sections: general and narrow. General AI (also called powerful AI) is what data scientists seek to extend in the future. It will be developed as a solution to broadly-defined problems in an intelligent method thanks to sophisticated simulated abilities and general learning abilities of its environment. Today that might seem like a science-fiction strategy – but it is becoming a reality sooner than later.

AI-based software and tools are already transforming the world as we know it in the shape of narrow AI, which concentrates on achieving particular tasks with incredible precision, often better than humans. For example, Image tagging software based on narrow AI can be used for tagging images on web platforms. And in the retail enterprise, AI-powered robotic technologies are being used to provide customer assistant services.

 

What type of data is most suitable for AI solutions, and how do you use them?

Developers who intend to design AI solutions need to train algorithms on a data set of diverse entries– for example, a cluster of images, text, or specific information like discrete transaction data or data from product interaction by the users.

You can however buy structured data, take advantage of public crowdsourcing enterprises (such as the Amazon Mechanical Turk), or – when handling potentially sensitive data – hire personal groups of data science professionals capable of helping you out with data collection, identification, and labelling services.

The dataset employed to train needs to include a good number of both positive and negative examples to help algorithms learn to differentiate them. For instance, if we want our model to identify traffic signals in pictures, we need to expose the model to pictures with signals and without them.

The developer or data scientist may test with different algorithms before sticking with the one with the best fit for the training data. We also require a test set – a dataset used to experiment with a model generated based on the training samples for evaluation, analysis, and improvement.

 

What is the future of AI and Machine Learning?

Earlier this year Gartner stated, “Artificial Intelligence and Machine Learning have arrived at a crucial tipping point and will from now on continue to augment and expand to every tech-enabled product, service, thing, or application.” They also expect that by 2025, AI will be amongst the top five asset preferences for a minimum of 30% of Chief Information Officers.

Consumers are now getting increasingly used to the usefulness of digital assistants, self-driving cars, robots working in factories, and smart cities. AI has created its impact on most industry sectors and persists to continue spreading to new industries.

Experts anticipate that Machine Learning will grow at an exponential rate. Some people believe that it will inevitably be joining the cloud-based service – so-called Machine Learning-as-a-Service (MLaaS).

Ultimately, Machine Learning will equip our machines to make better sense of data; both in its context and meaning. And these effective and actionable insights will drive AI-based solutions and make them a necessity for data-driven decision making and analysis among executives and project managers of the future.

 

What are the types of AI solutions?

It is fundamental for companies to look through the lens of business capabilities rather than technologies when it comes to AI. Speaking in terms of use cases, AI can be employed readily in three important business environments: automating business processes, gaining insight through data analysis, and engaging with customers and service professionals.

 

Process automation.

The most common type of AI implementation comes in the form of automation of digital and physical tasks—commonly back-office administrative and financial activities—using robotic process automation technologies. RPA is considerably more sophisticated than earlier business-oriented automation tools because the intelligence aspect of these “robots” function comparably to a human being entering and consuming information from several IT systems. Tasks include:

  • Reporting and storing data from e-mail and call centre systems into systems of records
  • Reaching into multiple systems to revise records and manage customer communications
  • Moderating failures to demand services across billing systems
  • “Updating” legal and contractual records using natural language processing.

RPA is the least pricey and most effortless to implement of the cognitive technologies in AI, and typically obtains a fast and high return on investment. It is especially well-fitting to operate across multiple back-end systems.

 

Cognitive insight.

This type of project uses algorithms to recognise patterns in massive volumes of data and analyse them to derive cognitive sense. Consider them to be “analytics on steroids.” These machine-learning programs are being used to:

  • predict what a particular customer behaviour on seller sites
  • manage credit fraud in real-time and find live insurance fraud
  • analyze warranty info to determine safety or quality concerns
  • automate tailored targeting of digital ads
  • provide intelligent insurance platforms with more-accurate and detailed modelling.

Cognitive insights gained by machine learning programs differ from those available from traditional analytics: They are usually much more data-rich and complex, the models are usually trained on a data set, and the models get more efficient and precise—that is, their capacity to use unexplored data to make predictions or place things into categories enhances over time.

 

Cognitive engagement.

Projects that deal with employees and customers use natural language processing chatbots, intelligent agents, and machine learning as their primary means of AI integration. This category includes:

  • intelligent representatives that offer 24/7 customer service addressing a wide and expanding array of issues — in the customer’s natural language
  • internal web pages for answering employee queries on topics including IT, employee benefits, and HR policy
  • product and service request systems for retailers that strategize to increase personalization, engagement, and sales
  • health guidance systems help providers create customized care programs

As businesses become more acquainted with cognitive tools, they are experimenting with aspects of AI that combine elements from all three categories to reap the full benefits of AI solutions deployment. A system with such deep-learning technology could engage its employees and customers alike using AI to list and suggest frequently asked questions and answers, previously resolved cases, and documentation to help find solutions to both employees and customer problems.

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