From autonomous cars to virtual bots on websites, there have been more artificial intelligence applications appearing in our day-to-day lives. However, AI has emerged as an evolving field that has driven significant changes across multiple sectors. As our artificial intelligence series ends, we shift our focus to exploring lesser-known applications in different industries. We also speculate on the future advancements of artificial intelligence and thought it would be fun to use what we call the AI stages of grief to explain how organizations adapt to these changes.

AI Applications Across Different Industries

1. Education

Artificial intelligence is revolutionizing education in numerous ways. This is evident in how the educational sector is transforming traditional teaching and learning methods by integrating AI. Given the global teacher shortage, the integration of AI into education enables automated grading and streamlines administrative tasks, giving teachers more time for valuable student-teacher interaction and engagement. AI-powered tutoring systems tailor the learning to individual students that adapt to each student’s learning style. The benefits of 1:1 tutoring is widely recognized as tutoring can help strengthen subject comprehension and build overall learning skills. Tutoring, a once inaccessible tool to many students, is now available to more people due to AI.

2. Human Resources

One of the most helpful AI tools has been its application in the recruitment and selection aspect in the hiring process. Because reviewing hundreds of candidates’ resumes and applications is a tedious and tremendously time-consuming process, AI tools serve to automatically screen resumes and identify qualified candidates. Another helpful characteristic of AI integration is its use in benefits administration. AI tools streamline benefits package comparison, ensuring optimal coverage and affordability. They autonomously verify employee eligibility, freeing up HR for strategic planning.

3. Healthcare

One of the most anticipated AI advancements can be seen in healthcare and medicine. AI aids in population health by empowering individuals to self-manage chronic conditions like asthma as it connects individuals to relevant screening and therapy. All this while also providing reminders for medication adherence and other necessary care steps. Additionally, a recent Mayo Clinic cardiology study found that AI effectively detected individuals at risk of a left ventricular dysfunction. In other words, identifying those who may be at risk of suffering from a weakened heart pump without displaying noticeable symptoms. This is just one example of the growing synergy between AI and medical research.

4. Agriculture

Artificial intelligence in agriculture is revolutionizing the way we cultivate crops, manage resources, and optimize yields. Multimodal AI integrates diverse data sources to provide farmers with comprehensive guidance on crop selection planting schedules, fertilization timing and harvest timing. With tools like Microsoft’s FarmBeats, farmers can learn about soil and yield maps, weather data analysis and predictive analytics to help them make informed decisions about crop management. These innovations hold promise for addressing challenges such as feeding and growing global population and mitigating the impact that climate change has on agriculture.

5. Finance

AI technologies empower financial institutions to automate tasks, analyze vast data sets, and make informed, data-driven decisions. This enhances operational efficiency and diminishes the risk of overlooking illicit fund flows. AI-powered surveillance further facilitates regulatory compliance by identifying trades that may contravene regulations. Moreover, AI enables intricate financial analysis and risk modeling by detecting correlations in extensive data sets that surpass human capacity. Consequently, it fosters informed decision-making in areas such as investments, lending, and insurance underwriting.

Future Impact and Implications

Generative AI has notably popularized artificial intelligence, demonstrating its value to businesses. However, throughout this article, we can see that artificial intelligence has an impact in many ways. In the IBM 2023 Survey, about 42% of enterprise-scaled companies report to have actively deployed using AI.

As such, it comes to no surprise that the demand for roles in artificial intelligence will rise. A few examples of said jobs are machine learning specialists, data scientists, or information security analysts. However, OpenAI supported by Microsoft, is conducting research on artificial general intelligence (AGI).

Amazon defines AGI as a field of theoretical research that attempts to create software with human-like intelligence and the ability to self-teach. The aim is for the software to be able to perform tasks that it is not necessarily trained or developed for. Although still in its initial stages, breakthroughs could potentially revolutionize the job market due to automation.

Another exciting development is the advancement of augmenting humans with implantable brain-computer interfaces (BCIs). These BCIs are designed to connect the human brain directly to computers, opening communication between the brain and external devices.

The leading pioneer in this is the company Neuralink, a neurotechnology company founded by Elon Musk. A part of researching this is to help those who are paralyzed such as Noland Arbaugh, who the first person to receive it around Jan 2024. Elon Musk real intention, however, is to have symbiosis with humans and AI.

With recent success, BCI is paving the way towards new developments in enhancing humans by integrating the capabilities of AI. While promising, BCIs also raise concerns about technological inequality due to high costs and access disparities.

Adapting to AI: Stages of Grief

With AI becoming an integral part of various industries, business leaders face the challenge of navigating an evolving technological landscape. The development that artificial intelligence is heading down naturally brings a range of emotional and practical responses. This can be because of the effect it could have on the job market to the effect it has on an individual level.

This section explores the stages of grief as we transition from traditional work settings to a future increasingly defined by AI. By examining the stages of denial, anger, and acceptance, organizations can make strategic decisions for a successful adaptation to AI.

Denial: Many individuals express apprehension about AI’s capabilities. The World Intellectual Property Organization noted that “the undirected use of AI could lead to substantial job losses in the short term.” Companies are not investing in AI as much as they should be due to a lack of understanding, fueling fear and skepticism.

Anger: Fear, mistrust, and resistance towards AI stem from factors such as the loss of human touch and ethical concerns. The Wall Street Journal highlights an incident with Levi Strauss & Co., which faced backlash for testing AI-generated images of diverse models, leading critics to argue it deprived real models of employment opportunities.

Acceptance: While concerns about AI replacing human workers are common, the reality is quite different. Change is necessary for progress but often brings fear and resistance. Preparing the workplace and addressing concerns can ease this transition. Encouraging a culture of collaboration between AI and human workers is essential for technological advancement.

Conclusion

In summary, artificial intelligence is transforming diverse sectors, from agriculture to finance. Its impact extends beyond just enhancing business operations; it also opens exciting future possibilities for AI. By exploring the stages of grief, organizations and individuals can gain valuable insights into the challenges and adjustments involved in adopting AI. With a strategic approach, organizations can navigate this transition smoothly and fully capitalize on the benefits AI offers.

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As we continue this series, this article aims to discuss the current state of the artificial intelligence landscape.

Artificial Intelligence Development

When examining the current state of artificial intelligence, let’s first look at the current state of development for artificial intelligence. After examining the history of artificial intelligence in our previous post of this series, we can see that many of the advancements in this field have been done in an academic sense.  Nowadays, artificial intelligence has moved away from only being developed in labs to now also being developed by businesses. According to Standford’s 2024 AI Index Report, industries take the lead with 51 notable machine learning models versus academia only contributing 15. Nvidia, Microsoft, and OpenAI are a few major contributors to artificial intelligence development as of 2024.

This can be contributed to the accessibility of artificial intelligence to the public, whether it be individuals or businesses. The accessibility to the development of artificial intelligence has transformed many industries such as healthcare, finance, and automotive manufacturing.

Role of Artificial Intelligence

With businesses now taking the mantle of developing artificial intelligence, business leaders now have more tempered expectations on the role artificial intelligence plays in their field. In part, this is due to artificial intelligence now being adopted into the workplace. While standalone tools like ChatGPT take the spotlight in today’s world of being revolutionary, businesses each have different takes on how they approach implementing AI.

In fact, IBM states that enterprise environments are looking at high-impact generative applications that enhance or integrate into their systems instead of replacing or revolutionizing. This can be seen by whether they develop their own in-house generative model or leveraging tools like “Copilot” in Microsoft Office.

Generative AI, however, isn’t the only artificial intelligence that has integrated into current society. Other pivotal subfields that contribute to the society we see today are machine learning, robotics, natural language processing, and computer vision. Some examples are cashier less-checkouts, website chatbots that offer advice, and personalize content in streaming platforms or social media due to better optimize algorithms.

Artificial intelligence offers a variety of benefits in many industries, but there also come new challenges with it.

AI Challenges & Issues

From the Standford’s 2024 AI Index Report, it indicates two challenges that we currently face in today’s climate: a lack of reporting standardization for AI and the increasing number of regulations for AI within the United States.

The lack of reporting standardization falls under the differing framework for evaluation in AI. This is due to how rapidly artificial intelligence develops within recent years and the differing application approaches that came from it. In fact, within the same report, it states numerous top leading developers like Google and OpenAI test their models against different responsible AI benchmarks. The lack of systematic approach now makes it hard to compare the differing models, as they operate under different standards.

Meanwhile, the way AI operates is why there has been increasing regulation for AI in the United States. The Standford report states that within 2023 there has been increase of 25 new regulations compared to only 1 in 2016. As AI uses enormous amounts of big data to operate their tasks, some of the data can either be private or sensitive. A prime example of this is the latest trend, Generative AI.

ChatGPT has gotten over millions of users since its release back in 2022. With users inputting data, ChatGPT has quickly refined its responses through the ingestion of the substantial amount of data added into its database. Employees, who can see the potential increase in productivity, may input enterprise data to leverage the tool for their own benefits. This data, potentially inside business secrets or copyrighted material, is now out to the public with a substantial risk of data violations.

With that, the next post will go more in-depth on the application of what is possible through AI.

Sources:
AI Index Report 2024 – Artificial Intelligence Index (stanford.edu)
The most important AI trends in 2024 – IBM Blog

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The journey of AI is a fascinating tale of human ingenuity, marked by groundbreaking research, technological advancements, and profound societal impacts. Join us as we briefly explore the evolution of AI, uncovering some key developments and challenges that have shaped this dynamic field over the decades.

The 1950’s and Alan Turning

Many consider the beginnings of AI to date from the mid-20th century and Alan Turing, a visionary who provided groundbreaking thought relative to artificial intelligence.  While playing an indispensable role in the advancement of modern computers, he also proposed a method to determine if a machine’s intelligence could be indistinguishable from a human.  This came to be known as the Turing Test. Despite only being a theoretical framework, the question on whether machines could think help conceptualize the idea of artificial intelligence before the term was coined.

1956: Dartmouth Summer Research Project on Artificial Intelligence

Soon after Alan Turing proposed his framework, John McCarthy and Marvin Minskey held the famous Dartmouth Conference in the summer of 1956. The proposal for this conference was a discussion about thinking machines and is now considered as the founding event of artificial intelligence as a field of study. Here McCarthy invited many brilliant minds such as Allen Newell, Cliff Shaw, and Herbert Simons who programmed the Logic Theorist.

The Logic Theorist was the first computer software engineered to perform automated reasoning, which was then dubbed as the first artificial intelligence program. This was the turning point for artificial intelligence as it changed from an idea into a feasible reality. The Dartmouth Conference is considered a seminal event in the history of AI as it sets the stage for future research and development in areas like machine learning and natural language processing.

1960’s – 1970’s: Formative Years

In the wake of the Dartmouth Conference, there was a surge of interest and research in artificial intelligence. A notable example is the establishment of AI studies at the Massachusetts Institute of Technology (MIT) by McCarthy and Minsky.  This lab became a hotbed of innovation and development in the field of artificial intelligence.

One early demonstration of such applications was by Joseph Weizenbaum, who created ELIZA in 1966. ELIZA, an early natural language processing program and one of the first chatbots, paved the way for applications like Siri and Alexa. Another significant development was the creation of the programming language LISP by McCarthy, officially released in 1960. LISP became a favored language for programming artificial intelligence.

Research was not limited to MIT as other important institutions, such as Stanford, Carnegie Mellon, and Edinburgh, were also making significant advancements in AI research. The advocacy and research efforts from these institutions incentivized the Defense Advanced Research Projects Agency (DARPA), a US government agency, to fund AI research, mirroring similar efforts by nations worldwide.

1970s – 1990s: AI winter

As government agencies’ patience drew thin, especially by DARPA with the slow development of the Speech Understanding Program, funding began to dry up.

As usual, lack of funding leads to lack of interest, and overall progress in artificial intelligence research slowed to a crawl. During these times, public sentiment towards AI was marked by skepticism and disappointment. A pivotal moment leading to the decline in AI enthusiasm was the publication of the 1973 Lighthill Report in the UK. This report, authored by Sir James Lighthill, criticized the progress of AI research, arguing that the achievements of AI were neither practical nor applicable as working systems.

Though the 1980’s encountered an AI revival driven by the development of expert systems, this resurgence quickly faded as these systems proved to be weak and expensive. Many of the problems came from the current computers of the time. Many researchers blame the lack of computer memory and processing power holding back applications from doing anything truly useful.

2000 – Present: Revival of AI

All progress did not stop, however. In 1985 Carnegie Mellon University began working on an expert system called ChipTest to play chess.  It then moved to IBM where it was renamed Deep Thought and then Deep Blue.  In 1996 Deep Blue lost to world champion Garry Kasparov 4 games to 2, but in a 1997 rematch, Deep Blue beat Kasparov winning 2 games and drawing 3.  This event became a pivotal moment in the advancement of computer processing, and the potential for artificial intelligence to surpass human intelligence.

AI was now headed to people’s homes.  A spin-out from the Stanford Research Institute’s Artificial Intelligence Center, Apple released Siri in 2010. A virtual assistant that can assist in everyday tasks, showcased the advancement of natural language processing and machine learning.  Alexa and others followed.

A step further came in 2022 with the release of ChatGPT, a chat bot built on GPT-3.5 large language model, emulating human conversations through text. Generative AI has now taken off as a field to popularize artificial intelligence capabilities through the use of deep learning and general access to big data,

These events renewed interest in artificial intelligence, leading to its current revival.  With advancing computing capabilities and the capturing of, and access to, incredible amounts of data, artificial intelligence has rapidly evolved to become a part of everyday life.

Seeing how far the field has come during its brief history, our next post will take a look at the current state of artificial intelligence.

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In this series of articles, we will provide a snapshot of the world of Artificial Intelligence (AI), from its terminology and origins to future trend predictions to help with understanding how this modern technology came to dominate the world’s conversation about our collective future.

Machine learning, deep learning, neural networks, supervised networks and more. These are some of the terms bandied about in articles and podcasts about AI.  We thought we’d start here – what do these terms mean in the context of a discussion about AI?

Let’s begin by understanding what Artificial Intelligence is at its core. IBM says, “Artificial intelligence, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.”  When used independently or in conjunction with other technologies like sensors, geolocation, or robotics, AI can undertake tasks that would typically necessitate human intelligence or involvement. Now, let’s take it a step further and define some of the basic terms and concepts.

Machine Learning

The folks at Google Cloud like to say Machine Learning automatically enables a computer or system to learn and improve from experience rather than via explicit programming. Machine learning uses algorithms to analyze large amounts of data, acquire insights, learn from them and make informed decisions.  Machine learning is not about teaching a computer to mimic human intelligence, it is specifically aimed at teaching a computer to perform specific tasks on its own.

Deep Learning

Deep Learning is a subset of machine learning. Unlike machine learning, deep learning removes the pre-processing of data and relies on patterns found within. This allows the ingesting of big unstructured data such as an image or a sentence without human intervention. This allows a computer to perform more complex tasks such as driving vehicles and picking the fastest route on the fly as traffic conditions change.

Neural Networks

Neural Networks are another important subset of machine learning and the backbone to deep learning. Inspired by how the human brain learns; Amazon likes to describe Neural Networks using interconnected nodes in a layered structure that signal each other as they pass data. This creates a system that is adaptive, creating an ability to learn from mistakes.  Thus, these artificial neural networks can attempt to solve complicated problems like summarizing documents or recognizing faces.

Natural Language Processing

Natural Language Processing (NLP) assists computer systems with understanding and interpreting human language. Using deep learning algorithms, Natural Language Processing is used in tons of daily activities – chatbots, speech recognition, and machine translation.

Robotics

Robotics systems are a form of artificial intelligence that can replicate or substitute human action of controlling physical objects. Applications for this subset can be found in the manufacturing industry as they automate the manufacturing processes.

Expert Systems

Expert system (knowledge-based systems) is a subset of artificial intelligence that stimulates the decision-making capabilities of a human expert within specific domains. This is done by analyzing a knowledge repository made by domain experts, with formats varying from databases to semantic network. An inference engine is built which deduces information from the rules and data provided through the knowledge repository, providing recommendations like an expert in the field.

Computer Vision

Computer Vision is a subset of artificial intelligence that teaches computer systems to process information from visual inputs such as digital images or videos. The computer systems are then able to make predictions and identify defects or issues.

In part 2 of our series, we’ll provide a brief history and evolution of Artificial Intelligence.

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