Artificial Intelligence (AI) is the intelligence demonstrated by machines, specifically computer systems. It is the development of a system in which operation(s) or behavior(s) conducted by a human are replaced by a non-human component, often incorporating programming and knowledge engineering. AI is usually limited or targeted in nature, with general machine-based intelligence remaining an elusive long-term goal. Machine learning and natural language processing (NLP), are disciplines within the broader field of artificial intelligence. Machine learning refers to processes in which an automated system can learn from data, rather than following a prespecified set of rules, and in many cases, can predict outcomes relating to the learned process. The aim of NLP is to extract information or derive meaning from human language (written or spoken) or to generate human language.
The term “artificial intelligence” is applied when machines use techniques generally thought of as intuitive human cognitive functions to achieve a goal. The question is, "How would the system attain intelligence without learning?". This question was answered earlier by IBM during 2001, where in it defined this can be achieved by introducing "Autonomic Computing Systems" possessing the following characteristics referred to as "self-*", "S-CHOP"
- Self-configuration: Automatic configuration of components
- Self-healing: Automatic discovery, and correction of faults
- Self-optimization: Automatic monitoring and control of resources to ensure the optimal functioning with respect to the defined requirements
- Self-protection: Proactive identification and protection from arbitrary attacks
The system learns from the data set changes it undergoes for every configuration change, for every error condition resolutions, thereby the system gaining intelligence by learning.
Applications of AI include (but are not limited to): advanced analytics, robotics, including in automated manufacturing; industrial controllers; self-driving cars; analysis of online commentary; stock market analysis; optimization of finance operations and stock investments; analysis of medical records; clinical decision support systems for medical diagnosis; automated interpretation of medical test data (e.g. ultrasound scan data); cybersecurity; intrusion detection – in software systems, communications networks and sensor systems; fraud detection; cyber-physical control systems; improvements in human-computer interaction; automated document classification, indexing and retrieval; customer recommendation systems; personalization of customer services; intelligent virtual assistants; search engines, including image-based search; translation services (including speech-to-speech translation).
What are we building with AI ?
AceInfo CoE team, with its proven experience in building enterprise class High Performance and High Availability systems/platforms, understands that when dealing with heterogeneous and distributed systems, it is mandatory to build some intelligence into these platforms so that the platform can orchestrate the components/infrastructure and cohesively react to interim needs for increased performance, Load balancing, elastic needs, fault tolerance, etc. We at AceInfo CoE constantly research and prototype several frameworks and methodologies that enable us to innovatively design and deploy systems that are autonomous and repeatable.
Our goal is to deliver intelligent applications, services and platforms to our customers by building reusable turnkey solutions. We enhance customer mission execution through intelligent automation and implementing predictive and proactive capabilities using AI.
AceInfo has been working with customers in our Emerging Technologies Lab, leveraging the publicly available data assets and developing "Natural Language Processing" to optimize search functions using Intelligence based pattern matching, Statistical methods-based word/data set scoring, and ranking. We extensively use the cloud based managed AI Services like Amazon Lex, Amazon Polly, Amazon Transcribe, Amazon Rekognition as well as ML Services like AWS Sagemaker, Google Tensorflow and other industry proven tools and frameworks.
Please check out our AI Turnkey Solutions and Prototypes.
- AI Bot for Text to Speech
- Facial Recognition and Media Analysis
- Social Media Sentiment Analysis
- Machine Learning Models/APIs
AI adoption journey
To Simplify and enable the AI adoption journey, we suggest the following steps to quickly and easily embed Artificial Intelligence into Applications Development.
- Step 1: Start Consuming Artificial Intelligence APIs: This approach is the least disruptive way of getting started with AI. Many existing applications can turn intelligent through the integration with language understanding, image pattern recognition, text to speech, speech to text, natural language processing, and video search API. There are multiple AI platforms that expose simple APIs at an affordable price point. Below is a sample list of the API providers: Amazon AI Services, Google Cloud ML Services, IBM Watson Services, Microsoft Cognitive Services, Clarifai, AIception, Algorithima, Lexalytics, Vize.it
- Step 2: Build and Deploy Custom AI Models in the Cloud: While consuming APIs is a great start for AI, it is often limiting for enterprises. Having seen the benefits of integrating Artificial Intelligence with applications, customers will be ready to take it to the next level. This step includes acquiring data from a variety of existing sources and implementing a custom machine learning model. It requires creating data processing pipelines, identifying the right algorithms, training and testing the machine learning models, and finally deploying them in production. This is when the enterprise should start investing in a data engineering and data science team.
- Step 3: Run Open Source AI Platforms On-Premises and/or in the Cloud: The next step in AI-enabling applications is to invest in the infrastructure and teams required to generate and run the models locally and natively. This is for enterprise applications with a high degree of customization and for those customers who need to comply with policies related to data confidentiality and data sovereignty. If ML as a Service (MLaaS) is similar to PaaS, running AI infrastructure locally is comparable to a Private Cloud. Customers need to invest in modern hardware based on SSDs and GPUs designed for parallel processing of data or leverage the cloud. They also need expert data scientists who can build highly customized models based on open source frameworks. The biggest advantage of this approach is that everything runs in-house and in the customers cloud space (single or multi-tenant). From data acquisition to real-time analytics, the entire pipeline stays close to the applications. But the flipside is in the OPEX and the need for experienced data scientists. Customers implementing the AI infrastructure generally use one of the open source platforms for Machine Learning and Deep Learning: MXNet, Microsoft Cognitive Toolkit, Tensorflow, Theano, Caffe and Torch.
If you want to get started with AI, explore the APIs first before moving to the next step. For developers, the hosted MLaaS offerings may be a good start. Artificial Intelligence is evolving to become a core building block of contemporary applications. AI is all set to become as common as databases. It’s time for organizations to create the roadmap for building intelligent applications. There are many technical approaches to AI, and with an even greater diversity for potential applications. Examples of relevant technical fields include (but are not limited to): deep learning; artificial neural networks of various types; supervised, semi-supervised and unsupervised machine learning; pattern recognition; image recognition; machine vision; fuzzy logic; uncertain reasoning using probabilistic methods; named entity recognition; sentiment analysis; natural language understanding; natural language generation; automatic summarization; language translation; analysis of structured or unstructured text; speech recognition; speech analysis; speech processing.