Is AI(Artificial Intelligence) making life better or confusing?

Artificial Intelligence: know how (series AI.101)

Wonder, a world without AI! With connected systems and AI driven applications we today manage a lot of our daily routines and mundane work by just a few clicks or even voice commands. AI adoption by techies through SaaS (Software as a Service) applications and tools has not only changed our way of living, but even our professional lives through business- process transformation and adoption by innovative leaders in large scale industries and domains. Mostly used in how one manages operations, processes, people, data analysis and predictive/ probabilistic outputs to make more smarter decisions and strategy.

Artificial Intelligence is a reality. AI companies (mostly in the areas of business services, general purpose applications) attracted nearly $40 billion globally in 2019. Investments made by major players across industries to deploy technology, data collection and AI solutions are continuous and immense through all their processes and projects.

Most of our business conversations, propositions, solutions and yes even resumes are now dominated by AI and ML (Machine Learning) jargons. Its hence important to understand the dynamics of AI, difference from ML.

Let’s start the series (Artificial Intelligence: know how) with basics first. Get started with Its progression and difference in applications basis underlying algorithms and its potential impact through different sectors.

What is AI?

AI indeed is notoriously difficult to define due to the conceptual ambiguities of “intelligence.” For this article, we take a broad view on intelligence as “the quality that enables an entity to function appropriately and with foresight in its environment.” AI can be considered an umbrella term covering a group of technologies that are capable of autonomously performing tasks that, if performed by a human being, would be considered to require intelligence.

Why and how AI is being adopted?

Process automation: Consistently perform high volume of repetitive work with reliable outputs without fatigue or failures

• Personalization at scale
• Fraud detection and risk mitigation
• Demand forecast and supply management
• Failure detection and mitigation
• Make your systems intelligent
• Gives higher accuracy: image processing, natural language processing, and advancements by integrating more data makes machines through automation and technology more accurate and intelligent in their insights and tasks
• Progressively learning algorithms: Machine learning algorithms to learn from the recent data, improved and self tweak thresholds to adjust and refine model outputs
• Deep learning- Structural causal inference: Ability to deploy and build neural nets that have many hidden layers.
• Enhancing your DATA worth: AI mines data and self-learning algorithms makes it “intellectual property”.

AI- architecture: concepts and models

AI is a field that evolves and develops on and from wide range of concepts, theories, methods and algorithms from different academic domains and fields

• NLP- Natural language processing: Programs that enable computers to process large amount of natural language. Technology can then be used to extract, synthesize, understand documents, and even communicate with end users.
• Image processing and Computer vision: It is an interdisciplinary scientific field (harnesses image data using models constructed with the aid of geometry, physics, statistics, and learning theory) that deals with how computers can gain high-level understanding from digital images or videos. It is then deployed for object identification, scene reconstruction, image restoration etc.
• Machine learning: Field that can allow computers perform tasks without being programmed to do so. Hence its highly logical in construct and methods overlap widely with the fields like data mining, optimization, generalization, statistics. Hence one finds its major adoption in analytical suits from data mining to predictive analytics and insights generation.
• Neural network: It’s a non-linear statistical data modeling or decision making tool that can be used to model complex relationships between inputs and outputs or to find patterns in data.
• Deep learning: Consider this as a neural net with multiple layers through which the data is transformed. They are generally interpreted in terms of probabilistic inference. Advancement is computer hardware with graphic processing unit (GPU), allows for more efficient methods to train deep learning algorithms. Its deployed to extract features to aid and build in the fields of computer vision, natural language processing, recommendation systems etc.
• Cognitive computing: Technology- hardware/ software that mimics the functioning of human brain. These systems are highly agile, they are adaptive, interactive, and contextual. Lot of work is being done in academia in the field of speech recognition, sentiment analysis, face detection, behavioral recommendations etc.

AI impact through different spheres:

• Economic: The economic impact of AI depends on whether there is sufficient investment to fund new AI initiatives and research and enable greater corporate investment. Investment in AI is growing rapidly but is still largely concentrated in the United States and China. Tech giants such as Google and Baidu spent an estimated $20 billion to $30 billion on AI in 2016. In 2017, according to CBInsights, $15.2 billion was invested in AI startups around the world, and nearly half (48 percent) of that total went to China; 38 percent was invested in the United States. The United States still has more AI startups than China, but China is making considerable headway in striking equity deals in the AI domain.
• Social: AI impacts society and individuals through the tech development and process enhancements. This changes the way people interact with their surroundings- their living style and working is all impacted.
• Sustainability: Both in the short term and long term, one expects AI to affect global productivity, equality, diversity and inclusion, and other environmental outcomes.
• Ethical/ cybersecurity: privacy safeguarding and inherent bias in training data used to build predictive AI systems for biased decision making are a few of the major drawbacks and threats one need to be mindful while deploying these tools.
• Environmental: Benefits aimed at using AI solutions in preserving the environment related to: Life on land, Marine life and Climate Action. With use case in the field of larger ecosystem, that will support and integrate low carbon energy systems with renewable energy and efficient systems etc.

AI brings the cultural transformation; it changes the status quo

About 85–87% of the data science projects are still in the POC stages. 60% of the CXO’s believe it to take at least 3–5 years to start seeing any ROI on their AI investments. As organizations embrace AI applications and deployment through change in tech framework and process re-structuring, they are changing the core and their ways of working.

Undoubtedly, AI investments define the leaders of future. To better capitalize on the benefits of AI, keep investing on your growth through innovations, people and re-vamping your business model.