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Artificial Intelligence is everywhere, opportunities are in abundance for cognitive enterprises. What do we mean by cognitive enterprises? Millions of ideas and think pieces are waiting to grow luxuriantly and cognitive AI technologies will play a bigger role in turning your ideas into a live piece of work.
It is expected that AI will bring simplicity to complex business issues and deliver more useful, engaging, intuitive, and profitable solutions, and this is what we say a cognitive approach for enterprises.
According to a report published by IDC a market research firm states that global spending on cognitive AI systems will reach $57.6 billion by 2021.
Biggest investors in cognitive AI systems are banking, retail, and manufacturing firms. Companies in these sectors will invest heavily in cognitive solutions for seeking innovations in better match services with clients requirements and handling fraud and risk detection.
On April 23, a Smartalk Webinar was also held by the ISG Group (Information Service Group) a Nasdaq listed advisory firm, on how businesses can achieve valuable outcomes by incorporating AI cognitive technologies into their operations.
The outcome of this webinar are as follows:
How are cognitive technologies being used in modern enterprises today? AI Cognitive technologies work on a diverse set of tools, techniques, and software that are able to make smart decisions in a complex business environment. It can be also referred to as the practical implementation of artificial intelligence and machine learning in your work operations.
Categorization of types of Artificial Intelligence (AI) cognitive technologies enterprises are deploying today according to their popularity index:
Robotics Process Automation — Robotic process automation help reduce the burden of often mundane and repetitive tasks and help businesses to automate certain tasks turned down by the workers.
Statistical Machine Learning — The set of statistical machine learning techniques automate analytical model building using ML algorithms that help businesses predict their customer behavior, fraud detection, and product recommendations.
Natural Language Processing — In the business domain, NLP helps in drawing data insights from a huge collection of data and helps in improving the overall user experience by creating smart NLP communication model and chatbots.
Expert or Rule-Based Systems — Today’s all of the leading enterprises want to implement “intelligent” business process management platforms that can help in reducing the need for manual processing in the complex management solutions.
Deep Learning Neural Networks — Deep learning is so helpful in solving complicated issues related to image classification and object recognition that is being used today in online gaming, product categorization, and fully automatic cars.
Physical Robots — Adding intelligent robots at a time can make your work better in terms of reducing the potential risk for errors and increasing productivity.
AI cognitive technologies increase the speed and reduce the cost of operations. Organizations today implementing cognitive technologies to automate processes, creating insights, and drawing conclusions from large and complex data sets that help make high-quality predictions and most likely help improve overall operations in your business.
According to reports by Statista, the estimate of global spending on cognitive and artificial intelligence (AI) systems in 2019 will be estimated to grow 13.5 billion U.S. dollars.
Research shows that enterprises today aim to grow their revenue rate by creating new products and new customer base rather than by cutting costs. They see cognitive technologies like AI, ML, NLP, deep learning, robotics as a way to reinvent their business and as a basis for business model transformation.
IBM is the best example of a company that pursued business transformation through cognitive technologies. Approximately 100 million U.S. Dollars invested in IBM’s idea of cognitive apps under the umbrella of IBM Watson Developers Cloud technology to build a robust business ecosystem for different vertical markets.
Cisco’s cognitive threat analysis is another great example of an advanced form of network security that works on a cloud-based solution set on the algorithms of artificial intelligence and machine learning developed for the discovery of threats inside a network.
Already there are numerous examples of AI cognitive technologies in the trade life cycle. There are at least a few more to roll out across the industry, yet more businesses still looking to adopt AI cognitive solutions. Fortunately, there are numerous tools and resources available in the market today for building cognitive apps.
IBM Cloud is a full-stack cloud platform that gives you a single point for the development and management of apps across public, private, and hybrid environments. The platform allows you to see where your apps and data live on the cloud. The tools on IBM Cloud help you safely connect to your Cloud environments, allow you to synchronize and transform data, and create enterprise APIs to the IBM Cloud catalog.
Smart Storage Options of IBM Cloud
You can choose public, private or hybrid storage according to the security needs of your firm.
Tool Choices for Control Options on IBM Cloud
You can select the level of control for your organization by choosing as-a-service options that include:
Software as a service (SaaS)
Platform as a service (PaaS)
Infrastructure as a service (IaaS)
Cloud Service Providers (CSPs) on IBM Cloud
This will help you manage and streamline the underlying infrastructure in your organization, and allow you to focus on software development and other development tasks.
On the Watson Developer Cloud platform, large data sets are easy to use and well-organized. Allow you to quickly develop and deploy chatbots and virtual agents across different channels. Operates on AI, unlock the hidden value in data to find the answers, monitor trends, and eliminating harmful bias.
You can try out these 3 options on Watson Developer Cloud:
Watson Discovery to monitor trends and to unlock the hidden value in data.
Watson Assistant to design, develop, and deploy chatbots.
Watson OpenScale to automate AI and eliminating harmful bias.
Elasticsearch in combination with Logstash, Beats, and Kibana that help you deliver actionable insights in real time and you can visualize the output based on the aggregations and filters you like. Elasticsearch is a lightweight platform to quickly get insight into large sets of data.
ELK Strack allow you to search for your app data using the Maps app in Kibana.
Organizations can try out new development features like cross-cluster replication in 6.7.
Keep track of changes made via AI and machine learning based on anomaly detection and alerting methods.
Demonstrate the value of data logs and metrics into a single platform.
Try out Elastic Cloud Enterprise 2.2 for role-based access control and a UI for cross-cluster search.
Torch support machine learning algorithms with its scripting language LuaJIT. LuaJIT is a scripting language that supports Torch’s scientific computing framework and having maximum flexibility in implementing complex neural network topologies. Using Torch you can develop an arbitrary form of graphs with neural networks, and place them on the CPUs and GPUs in an easy manner.
Torch offers you fast and efficient GPU support.
Support a powerful N-dimensional array and linear algebra routines.
Easy to embed with iOS ports and Android backends.
Support indexing, slicing and transposing.
So, that is all! However, there is more to come. If you want to implement a cognitive approach to your business, then the best way is to find a reliable software development firm that can help you understand the overall concept of AI cognitive technologies.
AI cognitive technologies is an extremely vast field of knowledge which possesses answers to your questions and also solves many problems that the business industry is confronting today and may offer you many solutions in the future for your business queries.
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