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How artificial intelligence and data analytics can help businesses thrive

aiuniverse — Thu, 10 Jun 2021 05:40:07 +0000

Source – https://yourstory.com/

Amid the constant disruption from unlikely competitors and changes in the industry occurring in faster and shorter cycles, time to market is constantly shrinking. To run a business and navigating this complexity in the present day and age, managers need relevant information and insights that can help understand the intended target audience, and their needs and preferences.

Thanks to the availability of multiple sources of market and customer data, analytics and artificial intelligence can be used to effectively respond to market dynamics, and drive revenue, profitability, and customer satisfaction. These new technologies are no longer a privilege for tech firms. An increasing number of companies are leveraging these tools to steer through unsettled waters and enhance their performance.

A few years ago, AI technology was being mostly used by early adopters. Any new technology typically faces a “chasm” in going from early adopters to the majority. With the pandemic and the inevitability of transformation, AI technology has “flown” over this chasm, entered the mainstream, and is now getting industrialised within companies.

There are several ways in which businesses can use AI and analytics to spur growth:

Customer monetisation

Analytics can be extensively leveraged to personalise the customer experience. The most optimum products and services can be offered at the right price and the experience can be optimised to the individual customers’ liking.

The impact of this work is amplified in the digital domain. Every interaction is being recorded which generates massive amounts of data, and this can be used to personalise the experience in real-time. All this leads to higher customer satisfaction and maximisation of revenue.

Optimised marketing spends

Companies spend a lot of money on marketing through various means and channels. It has been often said by CMOs – “I waste half the money I spend on advertising, I just don’t know which half.” Analytics and machine learning models can assess the marketing spend across channels to identify the optimum mix to drive revenue and brand equity. This can be fine-tuned by various customer segments and types.

Competitive advantage

Enterprises can collate data from within their organisation and the industry to have an upper hand in understanding the competition and market trends. By combining the information generated, organisations can get constant insights into sales, potential gaps in the market, and product improvement.

These insights enable the teams to work in collaboration, provide real-time responses to competitive tactics, and achieve better outcomes.

Optimisation of the supply chain

Analytics can be used to ensure that supply keeps up with the business with optimised costs, especially with the demands of digital business models which need short-time deliveries to customers.

The future of AI

It is impossible to anticipate every use case of AI in the future. Just like it happened with the internet, AI-based innovation will throw up use cases that we cannot fathom today.

Businesses will be able to leverage AI to answer complex questions around growth opportunities like new markets and product lines, and make multifarious decisions that are scientific and rooted in data.

Some exciting use cases are where the realm of AI is going beyond structured data to understand and analyse all sorts of unstructured data like images, audio, text, and video.

Using these techniques, AI is now even being used to optimise creativity and to help marketers decide what kind of creativity will appeal to specific audiences for specific campaign objectives.

However, the one key area where AI technology will impact the most is in disrupting entire industries and creating new business models. For example, what Tesla has done to the auto industry and what Netflix has done to the entertainment industry.

AI has a huge scope of disruption and transformation in areas like healthcare and education, and many others. All this is going to lead to transformational business opportunities for existing companies and new entrepreneurs.

The post How artificial intelligence and data analytics can help businesses thrive appeared first on Artificial Intelligence.

Seven Key Dimensions to Help You Understand Artificial Intelligence Environments

aiuniverse — Sat, 15 Jun 2019 10:19:36 +0000

Source:- towardsdatascience.com

Every artificial intelligence(AI) problem is a new universe of complexities and unique challenges. Very often, the most challenging aspects of solving an AI problem is not about finding a solution but understanding the problem itself. As paradoxically as that sounds, even the most experienced AI experts have been guilty of rushing into proposing deep learning algorithms and exoteric optimization techniques without fully understanding the problem at hand. When we think about an AI problem, we tend to link our reasoning to two main aspects: datasets and models. However, that reasoning is ignoring what can be considered the most challenging aspect of an AI problem: the environment.

When designing artificial intelligence(AI) solutions, we spend a lot of time focusing on aspects such as the structure of learning algorithms [ex: supervised, unsupervised, semi-supervised], the architecture of a neural network [ex: convolutional, recurrent…] or the characteristics of the data [ex: labeled, unlabeled…]. However, little attention is often provided to the nature of the environment on which the AI solution operates. As it turns out, the characteristics of the environment are the number one element that can make or break an AI model.

There are several aspects that distinguish AI environments. The shape and frequency of the data, the nature of the problem , the volume of knowledge available at any given time are some of the elements that differentiate one type of AI environment from another. Deep diving into those characteristics will guide the strategies of AI experts in areas such as algorithm selections, neural network architectures, optimization techniques and many other relevant aspects of the lifecycle of AI applications. Understanding an AI environment is an incredibly complex task but there are several key dimensions that provide clarity on that reasoning.

Seven Key Dimensions to Classify an AI Environment

One of the most effective methodologies for understanding an AI environment is to classify it across a series of well-known dimensions that are often only segmented in two or three classifications. Among the different characteristics that can be used to classify an AI environment, there are seven key exclusive dynamics that provide a rapid understanding of the challenges and capabilities needed by AI agents.

1-Single Agent vs. Multi-Agent

One of the most obvious dimensions to classify and AI environment is based on the number of agents involved. The vast majority of AI models today focus on environments involving a single agent but there is an increasing expansion in multi-agent settings. The introduction of multiple agents in an AI problem raises challenges such as collaborative or competitive dynamics which are not present in single-agent environments.

2-Complete vs. Incomplete

Complete AI environments are those on which, at any given time, the agents have enough information to complete a branch of the problem. Chess is a classic example of a complete AI environment. Poker, on the other hand, is an incomplete environments as AI strategies can only anticipate many moves in advance and, instead, they focus on finding a good ‘equilibrium” at any given time. Most of the famous Nash equilibrium principles are particularly relevant in incomplete AI environments.

2-Fully Observable vs. Partially Observable

A fully observable AI environment has access to all required information to complete target task. Image recognition operates in fully observable domains. Partially observable environments such as the ones encountered in self-driving vehicle scenarios deal with partial information in order to solve AI problems. Partially observable environments often rely on statistic techniques to extrapolate knowledge of the environment.

3-Competitive vs. Collaborative

Competitive AI environments face AI agents against each other in order to optimize a specific outcome. Games such as GO or Chess are examples of competitive AI environments. Collaborative AI environments rely on the cooperation between multiple AI agents. Self-driving vehicles or cooperating to avoid collisions or smart home sensors interactions are examples of collaborative AI environments. Many multi-agent environments such as video games include both collaborative and competitive dynamics which makes them particularly challenging from an AI perspective.

4-Static vs. Dynamic

static AI environments rely on data-knowledge sources that don’t change frequently over time. Speech analysis is a problem that operates on static AI environments. Contrasting with that model, dynamic AI environments such as the vision AI systems in drones deal with data sources that change quite frequently. Dynamic AI environments often need to enable faster and more regular training of AI agents.

5-Discrete vs. Continuous

Discrete AI environments are those on which a finite [although arbitrarily large] set of possibilities can drive the final outcome of the task. Chess is also classified as a discrete AI problem. Continuous AI environments rely on unknown and rapidly changing data sources. Multi-player video games are a classic example of continuous AI environments.

6-Deterministic vs. Stochastic

Deterministic AI environments are those on which the outcome can be determined base on a specific state. By determinism, we specifically refer to AI environments that ignore uncertainty. Most real world AI environments are not deterministic. Instead, they can be classified as stochastic. Self-driving vehicles are a one of the most extreme examples of stochastic AI environments but simpler settings can be found in simulation environments or even speech analysis models.

Understanding an AI environment is one of the most challenging steps on any AI problem. Luckily, the friction points across the seven dimensions explored in this article often yield a robust classification of an AI environment and facilitate the selection of models and architectures. While there have been notorious advancements in AI architectures and optimization techniques, the analysis of environments remains a highly subjective aspect of the AI lifecycle.

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