Are you interested in improving your organization’s ability to build AI systems, optimize decision making, and become much more valuable? Then continue reading to learn how our AI services and transformative experiences can help you use fewer resources to achieve cutting-edge results and evolve into an AI-first enterprise within 1-2 years. Most importantly, we’ll provide a roadmap on how to help architect your company to do the things that modern AI capabilities makes possible for you to do well.
Identifying Symptoms of an Academic AI Strategy
To begin, we will provide some simple questions to help you understand whether your organization is facing any of these AI pitfalls on your journey to adopting AI.
Is your organization unclear about the capabilities and limitations of AI technology?
Does your organization struggle with understanding the multiple types of data (unstructured, structured) that are available to build AI systems?
Does your AI team strategically acquire valuable data for your AI applications and data science projects?
Are you stuck using classical machine learning techniques instead of world-class deep learning architectures?
Do you struggle with identifying data that is valuable or not valuable for your business use case(s)?
Is your team unsure of how to handle messy data and data silos for your AI projects?
Are your executive and technical teams unsure of how to adopt AI or lack knowledge of the key components that make a company great at using AI?
Have you been unsuccessful in selecting or deploying valuable production AI systems and/or data science projects?
Do you want to build AI in your company but lack a systematic approach to successfully transform your organization to become good at AI?
Has your organization struggled with forming cross-functional teams that can develop sequences of innovative AI solutions for your enterprise?
Does your company lack knowledge of the implications of AI on society or not have a plan to consider ethics in your strategy?
If so, then this is a good article for you to help solve these issues. You can follow our upcoming multi-series of articles that demonstrate how any company can leverage funstematics.ai services and transformative experiences to create tremendous value in their industry vertical and evolve into an AI-first enterprise. Specifically, our process will also help you evolve from an academic AI strategy to one that is truer to real-world scenarios.
An Introduction to Applied AI
In this first article, we will address the pitfalls listed above by providing a realistic view of AI technology. We will cover important concepts such as the main forms of artificial intelligence (AI), machine learning and deep learning, AI systems versus data science, key techniques and applications, capabilities and limitations, and different forms of data. Then we will explain how AI is impacting society through automation, technical limitations, and exciting new products. Finally, we will illustrate the tremendous amounts of value it is creating, and how our services and transformative experiences can lead you to success on your AI journey.
The Age of Automation and Intelligent Systems
Artificial Intelligence (AI) is transforming the world around us by helping humans perform tasks easier, faster, and more efficiently. Many challenges that were previously deemed impossible to solve are now being solved with superhuman accuracy. Businesses are adopting AI-powered applications to transform every aspect of their organizations and achieve success.
AI presents huge possibilities regarding job creation, new discoveries, understanding climate change, boosting national security, improving quality of life, and increasing global productivity. Some of the most exciting applications today include recommender systems, conversational bots, autonomous vehicles, facial recognition, drug discovery, medical image diagnosis, robotics, precision agriculture, financial risk assessment, language translation, music generation, art creation, and games.
The implications of AI-driven automation on our society will be tremendous as many industries are already at risk of being disrupted. This foundational technology will also have a profound impact across every sector through the creation of new industries, services, products, and processes. In fact, AI is rapidly becoming the greatest transformative force in the economy and will undoubtedly produce one of the biggest revolutions that humanity has ever seen.
A Realistic View of AI Technology
Now that we have explained why there is a lot of excitement and value created from AI, next we will provide a realistic view of AI technology by explaining what artificial intelligence (AI) really means, the value it is creating, key applications, as well as its capabilities and limitations. Our process will demystify AI and help enterprises learn what AI can do so you can start thinking of how to apply this transformative technology to your problems. In addition, we will provide some valuable information on how to use AI to lower costs, increase revenues, optimize processes, leverage data as a strategic asset, and launch a new product or business.
Artificial Intelligence refers to two separate concepts: artificial narrow intelligence (ANI), and artificial general intelligence (AGI). Most of the value created today is from ANI (e.g., recommender systems, web search engines, smart speakers, autonomous vehicles, AI art creation, and facial recognition). AGI refers to AI that can do anything a human can do. With the rapid progress in ANI, there is a lot of hype that AI can do everything but that is not true. In fact, we may be several decades and technological breakthroughs away before true AGI is a reality.
The main drivers of value in AI today are machine learning and data. For machine learning algorithms to achieve the best performance, you would ideally use a large dataset and deep neural network algorithm known as deep learning. Some of the most important concepts in AI today are machine learning and data science. Machine learning refers to automatically mapping some input A to output B and usually results in a running AI system such as a website, mobile app, or autonomous device produced by an AI engineer or team. Whereas data science is about analyzing data to extract a set of insights to help make business decisions by a data scientist or team. The boundaries between these two concepts may be fuzzy and could overlap, but the output of a machine learning project will usually result in a piece of software that automatically learns to map some input to an output to automate a given task. Data science is the science of extracting knowledge and insights from data, and the output is usually a slide deck or presentation with a set of actionable insights that suggest hypotheses or actions.
Myths and Limitations
Despite the large amounts of value being created today with deep learning, there is still some uncertainty around its adoption due to some myths that are futile or untrue. Some of these uncertainties include that machine learning is a black box, needs too much data, that practitioners need a Ph.D., is useful only for computer vision, requires GPUs, and that it’s not "real" AI. These myths are untrue because machine learning is interpretable by visualizing gradients and activations. It can use less data with transfer learning and does not require a Ph.D. because most engineers use MOOCs and open source to develop machine learning skills. Also, state-of-the-art results are being achieved across all major applications ranging from computer vision to generative modeling, and only requires lots of GPUs for large research projects. Lastly, some believe it is not "real" AI because true AGI may be decades or hundreds of years away according to the leading AI pioneers today.
To help enterprises gain a better intuition of what AI can and cannot do, we will first discuss some of the technical limitations and ethical issues. Some of the weaknesses of machine learning today is that it may have performance limitations when learning complex tasks from small amounts of data. One of the barriers today preventing the acceptance of machine learning is Interpretability but there have been many advances in explaining the internals and output produced from these algorithms. Another weakness of machine learning is that it tends to work poorly when trying to generalize on new types of data that are different than what it has seen in the training dataset. To prevent some of these problems with machine learning, we recommend performing due diligence to ensure your concept is feasible before executing your project.
Other serious limitations of AI are that it could produce unintended consequences such as unnecessary hype, propaganda and disinformation, authoritarian surveillance, job displacement, harassment, and bias. As creators of data processing and intelligent systems, it is critical that you understand and research the major issues associated with the rise of AI such as discrimination and bias, adverse use cases, adversarial attacks, impact on developing economies, and the implications of automation on jobs. To overcome these limitations, it is important that AI teams incorporate ethics, and also have a realistic view that AI is already transforming industries but it can’t do everything.
AI-Powered Solutions and Transformative Experiences
Next, we will discuss some of the capabilities of artificial narrow intelligence. According to recent industry reports, AI is expected to create an additional $13 trillion in value by 2030 across industries such as manufacturing, transportation, retail, healthcare, materials, energy, and agriculture. A lot of this value will be created by applying the major AI application areas and techniques to a variety of data such as numbers, text, images, video, and audio.
These major applications areas include computer vision, natural language processing, speech recognition, generative modeling, network science, reinforcement learning, recommender systems, robotics, and geographic information systems/ geospatial AI. Some of the most useful techniques include classification, regression, clustering, anomaly detection, and knowledge graphs.
The best way to think about applying these applications is to think about automating common tasks that could be performed in under a second rather than entire job functions. Essentially, we recommend you perform some type of due diligence with AI experts and key stakeholders to determine technical feasibility and main drivers of business value.
Recent industry reports have also indicated that only approximately 29% of enterprises regularly use AI. This is a huge problem because many industries are already at risk of being disrupted by AI-first companies.
What We Do
To help solve this problem, funstematics.ai provides AI-powered solutions and transformative experiences for enterprises.
We Create Value in Two Key Ways:
AI services and products that help you produce running AI systems that automate tasks, and data science projects that optimize business processes and extract actionable insights for decisions.
Secondly, our AI transformation process for enterprises provide a roadmap to evolve into an AI-first company by developing pilots to gain momentum faster, creating a modern AI and data strategy, performing training, building in-house capabilities, aligning stakeholders, and considering ethics.
We are unique in that we provide all of the key AI application areas that a vertical company would provide such as computer vision, natural language processing, speech recognition, reinforcement learning, generative modeling, network science, GIS, and recommender systems for a variety of tasks or problems. Furthermore, our research is focused on creating value by developing brand new applications of AI using multimodal systems.
Get Started on this Fun and Exciting Journey into the AI Era Today!
If you are interested in learning how you can apply this transformative technology to build valuable applications and lead your enterprise into the AI era today, contact our sales team to get started. www.funstematics.ai
Conclusion
In this article, we provided a realistic view of current AI technologies, capabilities and limitations, the value it is creating, and it's impact on society. The next articles will go deeper into solving more of these challenges that enterprises face to help accelerate your AI adoption and ensure long-term success.
Is there a specific challenge or interesting topic you would like us to cover in a future article? Then leave a comment below.
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