Exploring the Dynamic Interaction between Humans and AI

A brief explanation of artificial intelligence technology

Artificial intelligence (AI) primarily concentrates on prediction, encompassing a wide range of algorithms beyond mere task automation. It can even be applied to automate writing processes. The essence of artificial intelligence lies in its ability to learn from numbers and make accurate predictions.

This holds for large language models, which serve as word prediction engines at their core. However, there are cases where the predicted word may not align with factual accuracy for a specific question. Despite this, subsequent predictions continue along a path that may deviate from accuracy yet skillfully construct a cohesive series of words.

Also, artificial intelligence incorporates machine learning and deep neural learning algorithms that extend beyond predictive capabilities. These algorithms are instrumental in forecasting critical events such as engine failures or customer attrition in specific companies. Even within human resources, AI algorithms are crucial in predicting employee churn. Although automation falls outside the realm of AI, prediction remains its fundamental domain.

Artificial intelligence is not automation, and automation can be achieved without artificial intelligence algorithms. For instance, Robotic Process Automation which automates copying and pasting information doesn’t require artificial intelligence. It can replace humans, bring rapid progress, and answer complex questions, but no ai is needed. The situation is different. If we want to predict the next task, we want to automate or predict how many invoices will be settled next month. In this case, the artificial systems are expected to be engaged.

Rise of AI systems and their impact on various domains

AI methods have transformed various domains, profoundly impacting society and shaping how we live, work, and interact. In healthcare, AI has bolstered diagnostic accuracy, enabling early detection of diseases and personalized treatment plans. An exciting healthcare example is Butterfly Network.

The company produces a handheld ultrasound system that connects to smartphones. The team obtained FDA 510(k) clearance for its Auto B-line Counter. This cutting-edge technology utilizes AI to assist healthcare providers in assessing lung function abnormalities. This breakthrough development of artificial intelligence holds the potential to enhance diagnostic accuracy and improve patient care by providing healthcare professionals with valuable insights for early detection and personalized treatment plans in cases related to lung function abnormalities.

Another example is robotic assistants powered by AI that are capable of improving patient care and performing complex surgeries with precision. Intuitive Surgical is a company that has achieved excellence in using robotic assistants powered by AI to improve patient care and perform intricate surgeries along with other tasks. One of their most notable achievements is the daVinci Surgical System, a cutting-edge robotic surgical platform that combines robotics, advanced imaging tools, and artificial intelligence. This platform, designed and created by amazing AI scientists, empowers surgeons with increased precision, control, and agility during minimally invasive procedures.

Financial institutions have also benefited from AI algorithms, optimizing investment strategies, detecting fraud, and enhancing customer experiences through personalized recommendations. Furthermore, AI has revolutionized education by paving the way for intelligent tutoring systems that provide tailored learning experiences for students. AI algorithms analyze user preferences in entertainment to offer personalized content recommendations and generate realistic virtual environments. However, integrating AI raises ethical concerns such as privacy, bias, and job displacement. Therefore, it is essential to navigate these challenges responsibly, ensuring that AI technologies are designed, implemented, and regulated to benefit society as a whole.

Of course, I am not able to analyze every vertical and present artificial intelligence world and explain every possible example of AI-powered solutions enhancement. However, I think the direction can also be explained by looking into where the money goes:

Source: State of Ai 2022 and Dealroom.co

Purpose of the article: exploring the collaboration between humans and AI

Back in 2018, McKinsey did some research on artificial intelligence and found some interesting summaries. I know it’s a five-year-old study, but I’m more interested in what actually happened and how AI made money. I feel like I’m allergic to the futuristic talk and possibilities of AI. I want to know the real deal so we humans can understand where the productivity opportunities sleep.

So, McKinsey looked into different industries and found some practical examples of how AI was working and making an impact. It’s good to know about the past successes of AI because it gives us a better idea of what it can do and how it can be useful in different areas.

Source: McKinsey, Business Functions in which AI has been adopted by industry (% of respondents)

After that, inquiries arose regarding how humans can effectively utilize intelligent systems and ensure that a larger number of individuals can collaborate and expand it further.

This article aims to explore the future of human and machine interaction, discover ways to enhance it, and encourage more people to collaborate, thus improving team, enterprise, and workplace productivity.

Enhancing and Understanding Human-Centered Communication

In today’s fast-paced work environment, efficient and productive information flow drives organizational success. However, many people are experiencing a significant productivity disruption according to research and reports from Microsoft.

According to surveys, virtual interaction has proven to be particularly challenging for certain aspects of collaboration. Most respondents (58%) need help brainstorming effectively in a virtual environment where technology drives the entire process. Additionally, a significant percentage (57%) feel that catching up is a struggle, influencing work comfort and mental health. Specifically, most people reported that the end of meetings often leaves them needing clarification on the following steps (55%) and finding it hard to summarize the key outcomes (56%).

Furthermore, the stats reveal an astonishing increase in meetings and calls held on collaboration platforms since 2020. Teams meetings and calls have surged by a staggering 192% per week. These statistics highlight the urgent need to improve the quality and effectiveness of virtual touch to reclaim lost productivity and streamline workflows. Systems significantly influence human interaction, and it is essential to remember that technology should not confine or restrict individuals.

The emergence of artificial intelligence has the potential to revolutionize the interaction experience, transforming how people interact and collaborate. With AI and information technology integration, information flow becomes more than just exchanging messages; it evolves into a dynamic and enhanced process that transcends traditional boundaries.

Whether participating synchronously or asynchronously, AI-powered interaction enables individuals to access and review discussion content in a way that best suits their workflow and preferences.

Role of natural language processing in human-AI Interaction

Natural Language Processing (NLP) is an integral part of artificial intelligence that concentrates on enabling computers to comprehend and respond to both written and spoken language, mirroring human-like abilities.

NLP plays a crucial role in cognitive computing, which empowers computers to gather, analyze, and understand numbers and stats effectively.

By combining computational linguistics, machine learning, and other learning techniques, NLP enables machines to process information like human comprehension, capturing the intention and emotion behind spoken or written words.

NLP powers various applications, including language translation software like Google Translate, voice-controlled virtual assistants such as Alexa and Siri, and GPS systems.

This technology can be categorized into four main areas, which include 

• Text classification, 
• Text extraction, 
• Machine translation, 
• Natural language generation. 

With text classification, labels can be assigned to texts to classify them based on their content, revealing the emotions conveyed in a text’s narrative and the writer’s or speaker’s intentions. 

Text extraction involves analyzing and extracting keywords or essential text information, aiding in data analysis and retrieval. An example of text extraction is when a company uses natural language technology to analyze customers’ reviews and extract keywords related to product features or customer satisfaction. This process helps in information analysis and retrieval, providing valuable insights into customer preferences and identifying areas for improvement.

Machine translation enables automated text translation from one language to another, bridging the language gap and enabling cross-lingual interactions. This aspect of Natural Language Processing plays a crucial role in niche language processing, such as the preservation and support of indigenous languages.

Finally, natural language generation involves (mostly known thanks to ChatGPT or Bard):

• Collecting and analyzing unstructured information to generate content,
• Synthesizing information,
• Creating coherent narratives or text outputs based on the analyzed information. 

These NLP functions grant humans an unparalleled level of power through technology. So the question is:

How can humans leverage the fantastic nature of NLP science and use it for interaction?

Humans can harness the incredible potential of Natural Language Processing science to revolutionize interaction in countless ways. Here are only a few:

• NLP enables machines to comprehend and generate human language, bridging the gap between humans and computers. The emergence of platforms like abacus.ai introduces a groundbreaking opportunity to transform our interaction with machines. These platforms allow users to input prompts such as “Write me a sequence that takes Variables A and puts it into model B.” In response, the platform automatically generates the corresponding code. This revolutionary approach transforms our engagement with machines by streamlining the development process and eliminating the need for manual coding while humans can concentrate on more creative work.
•  NLP can unlock even more sophisticated applications, such as natural language interfaces for complex tasks–Klarna company, a buy now pay later business, offers interaction through a conversational interface. Instead of searching for sunglasses or a soccer ball, users can write, “Klarna, give me the best soccer ball for kids,” and the results will be sorted out quickly which reduces the time required for classic Googling. Klarna is capable of doing so, as artificial intelligence “interacts” with machines (servers) and controls the querying process, information flow, and results separation
• NLP not only empowers the writing aspects but also influences the spoken way. Here is the photo of my car interface.

I just asked, “Hey, Siri, order Starbucks for me.” Options popped up, and with simple “Option 2,” my Pike with Almond milk was waiting on the counter. I can focus on driving while AI systems send prompts to recognize my voice, separate noise from the context, etc.

If we take it to the manufacturing level, there is no need for buttons and switches. On the touch screen, the operator will write a prompt, “Now switch to quality control mode and separate flawless products from the failures,” and the whole production chain will react.  

AI’s potential to assist in mental health and human well-being

A team comprising researchers from IBM and the University of California has conducted comprehensive research of 28 studies focused on artificial intelligence systems in mental health. Their findings indicate that, depending on the specific AI technique employed and the quality of the training data, these algorithms exhibit an impressive accuracy range of 63-92% in detecting various mental illnesses.

In addition, mental health tracking solutions that utilize AI technology are also playing an important role, along with good data. These products may be paired with wearable devices to track vital signs such as heart rate, blood pressure, and oxygen levels. It’s not the future. This technology is available to customers.

A good example would be BioBeats (acquired by Huma mental health app which uses artificial intelligence to analyze sensor inputs from wearables). Its purpose is to assist organizations in preventing employee burnout, and it has been reported to decrease the frequency and duration of sick days by up to 31%.

Anxiety, a state closely tied to one’s well-being, can be a potential cause of dizziness. An intriguing illustration of this is the Mayo Clinic, which harnesses the power of artificial intelligence in diagnosing and managing dizziness. I highly recommend listening to this podcast to delve deeper into the topic: https://mayoclinictalks.podbean.com/e/utilizing-artificial-intelligence-to-evaluate-dizziness/

Based on the above research and my experience I can only tell (not discovering a gold rush again) that Deloitte was right (figure below) and the market of automated help with artificial intelligence will only grow. Human-AI collaboration power is here and will only increase which will create a shift in productivity. Less mental health care issues mean fewer challenges and a better work world.

Source: Deloitte

Improving human touch through AI tools and systems

Can AI improve how we talk, write, and extend our “touch” possibilities? Can human intellect squeeze more and get communication to the next level with the help of AI systems?

Let’s see what Jeff Hancock, Professor of Communication at Stanford University once said:

“Most people have experienced some kind of AI communication, the most common being the simple, smart replies in email messages that provide options such as “that sounds good,” “that’s great,” or “sorry, I can’t.” What we found is that even if you don’t use those AI-generated responses, they influence how you think. Those three options prime you. When you write an email back, you tend to write a shorter email. You tend to write a simpler email. And it just isn’t linguistically less complex: You have more positive affect, which means you use more positive emotion terms and fewer negative ones. That’s because that’s how smart replies are built: They’re very short, they’re very simple, and they’re overly positive. You don’t even have to be using these systems to actually be affected by them.”

Thanks to the widespread availability of Generative AI tools, we find ourselves in a fortunate position to harness these tools and significantly enhance our productivity. Various systems such as otter.ai, reclaim.ai, and taskade.com allow us to streamline our work processes and unlock new levels of efficiency and output. These tools empower us to accomplish tasks with greater ease and effectiveness, allowing for seemingly endless possibilities to maximize productivity.

I can sense a clear correlation between Professor Hancock’s statement and the usage of AI systems without self-reflection. Recognizing that these systems are not independent, fully automated, or self-sufficient is crucial.

We must remain engaged and mindful and do our research whenever we rely on artificial intelligence to perform tasks. The process begins with a prompt or command, triggering the AI engines. The possibility of delivering poor results is around the corner.

The outcome can be disastrous if we provide a low-quality prompt or misconfigure settings in systems like reclaim.ai. Others will notice and experience the negative consequences, potentially resulting in a decline in overall efficiency. It is therefore vital to approach AI utilization with care, ensuring that we actively consider the inputs and settings to optimize the desired outcomes.

Examples of AI research in healthcare and its benefits

Diagnosis and treatment applications – AI pursuit of diagnosing and treating diseases has been a prominent area of interest since the 1970s with the creation of MYCIN at Stanford University to detect blood-borne bacterial infections (source: HBR). These early systems did not significantly surpass human diagnosticians’ capabilities and their integration with clinician workflows and medical record systems needed improvement. In the present era, the landscape of disease diagnosis and treatment has evolved considerably with the advancements in artificial intelligence technology. AI is now playing a pivotal role in transforming healthcare by augmenting the capabilities of medical professionals and improving patient outcomes. Numerous innovative projects and initiatives have emerged, showcasing the potential of AI in this field.

One notable example is the application of deep learning algorithms in medical imaging. These learning models, trained on vast amounts of numbers, can analyze medical images such as X-rays, CT scans, and MRIs to identify anomalies and provide accurate diagnoses.

Zebra Medical Vision, Aidoc, and PathAI are prime examples of companies that extensively utilize artificial intelligence for diagnosis and treatment prediction in healthcare and contact with doctors and patients.

Zebra Medical Vision harnesses AI to analyze medical imaging data, offering automated insights to radiologists. Their algorithms can identify and prioritize critical findings in imaging scans, including brain bleeds, fractures, and suspicious lesions. Zebra Medical Vision improves efficiency and enhances patient care by assisting radiologists in the diagnostic process.

Aidoc specializes in an AI-powered radiology platform that analyzes medical images like CT scans. Their algorithms detect and prioritize critical abnormalities, such as intracranial hemorrhages and pulmonary embolisms. By enabling radiologists to identify and triage urgent cases swiftly, Aidoc’s technology facilitates faster and more accurate diagnoses.

PathAI specializes in the application of AI for pathology and pathology-related research. Their platform aids pathologists in analyzing and interpreting histopathology slides, leading to improved diagnostic accuracy and efficiency. PathAI’s technology shows immense potential in assisting with diagnosing cancer and other diseases through the analysis of tissue samples.

It’s hard to say and quantify how big this market is, but only by looking at CBInsight rankings of the best of the best we can see at least 150 companies (carefully selected). Here is the source.

Patient engagement and adherence applications – Notable examples include MyChart, which provides patients access to their medical records, appointment scheduling, and secure messaging with providers. Another example is Mango Health, an application incorporating medication reminders, pill identification, and a reward system to encourage medication adherence. The benefits of solving these kinds of issues are apparent – streamlined information flow, customers with instant health support, and knowledge accessible almost immediately.

Patient engagement and adherence applications have seen significant growth and innovation in recent years, driven by technological advancements and a growing recognition of the importance of patient involvement in healthcare.

Here are some key achievements in this field:

• Mobile Health (mHealth) Platforms: The widespread adoption of smartphones and mobile devices has facilitated the development of patient engagement and adherence applications. These platforms offer user-friendly interfaces, personalized experiences, and real-time interaction, allowing patients to access healthcare information and engage with their care team anytime, anywhere.
• Personalized and Tailored Experiences: Recent advancements in data analytics and data management have enabled patient engagement applications to provide personalized and tailored experiences. These applications can analyze patient data, preferences, and health history to deliver targeted educational content, medication reminders, and behavioral interventions specific to each individual’s needs and circumstances (Apple Health Apps are a good example).
• Integration with Wearable Devices: Patient engagement applications now often integrate with wearable devices, such as fitness trackers and smartwatches. These integrations allow patients to track their health metrics, including physical activity, heart rate, and sleep patterns. By leveraging this data, applications can provide personalized insights, goal setting, and feedback, motivating patients to stay engaged and adhere to their treatment plans. Popular apps can improve human contact, especially with doctors by integrating apps with healthcare providers (see the picture below; it’s a screenshot of my smartphone screen)

• Gamification and Behavioral Incentives: Many patient engagement applications now employ gamification techniques to increase patient motivation and adherence. These applications transform treatment adherence into an interactive and engaging experience through challenges, rewards, and progress tracking. Gamification elements can be particularly effective in improving adherence among younger patients and those managing chronic conditions (Streaks app, not strictly a health care app, has many built-on habits which support mental health and human-centered design and can send data from Streaks app to f.i. Apple Health Apps)
• Remote Monitoring and Telehealth Integration: With the rise of telehealth, patient engagement applications have increasingly incorporated remote monitoring capabilities. Patients can use these applications to share vital signs, symptom updates, and other health data with their care team remotely. This integration enables healthcare providers to monitor patients more closely, identify potential issues, and intervene in a timely manner.
• Improved User Experience and Interface Design: Patient engagement applications’ user experience and interface design have significantly improved. Developers increasingly focus on intuitive navigation, user-friendly layouts, and clear instructions to ensure patients can easily access information, understand their treatment plans, and interact seamlessly with the application. A great example, in my opinion, is this app. It has a lot of “empathy” inside but is designed for customers–a human-centered approach.

Administrative applications – AI systems have revolutionized numerous industries and healthcare is no exception. In healthcare administration, AI applications have proven incredibly beneficial by offering streamlined processes, improved accuracy, and enhanced patient care.

According to a survey conducted by Morning Consult, a significant finding revealed that 49% of US healthcare executives believe that AI and machine learning (ML) are highly effective in improving operational performance, health system efficiency, and organizational performance. This statistic highlights the growing recognition among healthcare leaders of the transformative potential of AI and ML technologies.

Healthcare organizations can streamline their operations, optimize resource allocation, and enhance administration by harnessing these advanced technologies’ power and other things technology related. The survey results underline the increasing confidence in AI and ML as essential tools for driving efficiency and effectiveness in the healthcare industry.

Implications for the healthcare workforce

The increasing effectiveness of AI and machine learning (ML) in healthcare has profound implications for the healthcare workforce. While these technologies offer significant advancements in operational performance, health system efficiency, and administrative performance, it is crucial to balance their implementation with considerations of humanity, the art of medicine, and the importance of human interaction.

As AI and ML automate routine tasks, healthcare professionals can focus more on the compassionate aspects of care, harnessing their expertise and empathy to deliver personalized and holistic treatments.

Furthermore, integrating AI and ML in healthcare should be viewed as a complement to the science of medicine rather than a replacement. By leveraging these technologies as tools, healthcare providers can augment their clinical decision-making, enhance diagnostic accuracy, and improve patient outcomes.

Can artificial intelligence take over healthcare?

In my opinion, human intellect will always be ahead of AI systems. It’s undeniable that the human mind has a remarkable capacity for creativity, emotional intelligence, and critical thinking abilities that AI systems have yet to surpass.

While AI is exceptional at performing specific tasks, human intelligence encompasses a wider range of cognitive abilities. Our understanding of the world is shaped by our experiences, emotions, and values, allowing us to navigate complex ethical dilemmas with empathy.

Moreover, human intelligence is not limited to rational thinking, as our intuition and creativity play a vital role in solving problems. It’s worth noting that humans are AI’s creators and drivers, ensuring its maturation remains grounded in our capabilities.

Human intelligence’s multifaceted nature and innate qualities make it superior to AI systems that remain tools designed to assist us rather than replace us.

Ultimately, the goal should be to leverage AI and ML technologies to empower healthcare professionals to deliver exceptional care while prioritizing the well-being and satisfaction of their patients.

Intelligent Systems and Complex Tasks

Machine learning and deep learning in solving complex tasks

ML and DL create robust solutions for complex tasks. These techniques, developed by universities and researchers, enable computers to learn from data and make predictions or decisions without explicit programming. They uncover intricate patterns and provide valuable insights by analyzing vast amounts of data.

For example, in image recognition, DL algorithms can accurately classify objects in photographs (i.e. breast cancer screening).

Machine learning enables chatbots to understand and respond to human language in natural language processing (example: https://www.druidai.com/chatbots-for-healthcare-pharma and their symptom checker)

In healthcare, deep neural learning models have been used to analyze medical images and assist in diagnosis. These techniques transform industries by optimizing processes, improving decision-making, and fostering innovation in data-rich environments.

AI’s ability to effectively deal with time-consuming processes

Process redesign is paramount in achieving operational excellence and optimizing organizational efficiency. With the advent of artificial intelligence, organizations can automate and optimize processes to a high degree, resulting in enhanced performance and productivity.

AI developed through extensive research has revolutionized process redesign by providing valuable insights and automation tools that help streamline operations, reduce costs, and improve customer satisfaction.

In the next section of this article, I have prepared several examples highlighting humans’ importance in leveraging AI technologies effectively.

Human expertise, empathy, and contextual understanding complement AI’s capabilities to drive positive outcomes. By combining the power of AI with human judgment, research, and science, organizations can harness the potential of AI for the common good, achieving operational excellence while ensuring ethical and responsible practices.

Case studies demonstrating AI’s impact on specific tasks

• Predictive Maintenance: AI algorithms predict machine failures, enabling proactive maintenance and reducing downtime and repair costs. Human expertise combines with AI insights to ensure optimal decision-making in a contextualized manner.
• Fraud Detection: AI analyzes data patterns to detect and prevent fraudulent activities, saving organizations from financial losses and reputational damage. Human oversight and domain knowledge complement AI’s capabilities to ensure accurate and ethical outcomes.
• Customer Service: AI-powered chatbots provide automated assistance, reducing wait times, improving response times, and enhancing the customer experience. Human intervention ensures empathy, understanding nuanced customer needs, and handling complex situations beyond the scope of AI.
• Inventory Management: AI predicts demand patterns and optimizes stock levels, reducing inventory costs and improving supply chain efficiency. Human decision-making contextualizes AI insights considering market dynamics and supplier relationships.
• Robotic Process Automation (RPA): Software robots automate routine tasks, reducing errors, increasing efficiency, and saving time and money. Humans contribute by designing and overseeing RPA systems focusing on strategic and creative endeavors.
• Resource Allocation: AI optimizes resource allocation by analyzing data patterns, improving efficiency, and reducing costs. Humans provide context-specific expertise, considering broader organizational goals and the impact on stakeholders.
• Workflow Optimization: AI identifies inefficiencies and automates processes, improving efficiency, reducing costs, and enhancing output quality. Humans bring a deep understanding of the process and organizational dependencies to guide AI-driven changes effectively.
• Predictive Analytics: AI algorithms analyze data patterns to provide insights into future trends and risks, aiding better decision-making. Human interpretation of these insights adds critical context, education, and domain expertise, enabling nuanced and responsible actions.
• Supply Chain Management: AI predicts demand, adjusts stock levels, and automates processes, improving efficiency and reducing costs. Humans ensure alignment with ethical and social considerations, such as sustainability and fair trade practices.
• Process Mining: AI analyzes event logs to identify process inefficiencies, leading to increased efficiency and cost savings. Human experts leverage AI findings to propose and implement improvements, considering the broader context and stakeholder needs.

Human-Centered AI and Ethical Considerations

Importance of developing AI systems that prioritize human needs

In the rapidly evolving landscape of technology, the evolution of artificial intelligence systems holds great significance, particularly when prioritizing human needs.

I want to mention a vital element. It becomes crucial to emphasize the importance of designing and deploying AI systems that align with the fundamental values of empathy, science, ethics, and control. Empathy is what most people expect. Nothing will replace a handshake, hug, or simple active listening.

People might generally be afraid of artificial intelligence because the media pictures it as cold, blood-sucking robots which want to take over the world and steal our territories. We need to remember that doctors, nurses, firefighters, and policemen are trained to behave in a specific way, the way AI can not develop, handle and maintain.

Ensuring AI technologies save time and enhance human capabilities

One of the critical aspects of developing AI systems that prioritize human needs, empathy, and diversity is ensuring that these technologies save time and enhance human capabilities. By harnessing the power of AI, we have the potential to automate mundane and repetitive tasks, freeing up valuable time for individuals to focus on more meaningful and creative endeavors.

Whether streamlining administrative processes, analyzing vast amounts of data, or assisting in complex decision-making, AI can be a transformative force in increasing efficiency and productivity for individuals and organizations.

Addressing concerns of AI replacing humans in certain domains

However, it is essential to address concerns surrounding the potential replacement of humans in certain domains. While AI can undoubtedly perform specific tasks with remarkable precision and speed, it should be approached as a tool to augment human abilities rather than as a substitute for human involvement.

By leveraging AI technologies to complement human skills and expertise, we can create a symbiotic relationship that leads to the best outcomes. This approach allows us to leverage the power of AI while preserving the unique qualities and insights that only humans possess, such as creativity, intuition, and empathy.

When developing AI systems, it is paramount to consider the ethical implications that arise from their deployment. Ethical guidelines and frameworks should be embedded within the fabric of AI development processes to ensure responsible and accountable practices (please check the next section of this post).

The potential biases and unintended consequences that can emerge from AI algorithms must be actively addressed to mitigate any harm they may cause. Transparency, fairness, and inclusivity should be the guiding principles when creating AI systems that respect and reflect their users’ diverse needs and values.

Moreover, fostering empathy is essential when developing AI systems prioritizing human needs. By understanding and empathizing with the end-users, whether customers or individuals interacting with AI technologies, we can ensure that their experiences are at the forefront of system design (hospital patients don’t care which hospital they go to. They want to be served professionally regardless the skin color or accent).

AI systems should be built to empower and assist individuals, striving to enhance their lives and well-being rather than exploit or manipulate them for financial gain or other ulterior motives.

Lastly, establishing effective control mechanisms is vital for the responsible development and deployment of AI systems.

No black boxes like ChatGPT or Bard.

This includes designing AI systems with explainability and interpretability in mind, allowing users to understand and challenge the outcomes produced by these systems. Transparency in the decision-making process of AI algorithms can help build trust and foster collaboration between humans and AI systems. The complete documentation (explained visually in the storytelling ways) should be obligatory, so everybody can understand how those systems work.

Ethical implications and guidelines for responsible AI development

There is an interesting paragraph in Soheil Human, Ryan Watkin’s article

“Likewise, attempting to develop AI that responds to and/or are responsive to underprivileged communities (whether based on race, gender, ethnicity, economics, or combinations of these and other variables) demands a multi-disciplinary understanding of human needs–integrating multiple ‘levels’ (individual, organizational, societal needs). In other words, needs can fundamentally contribute towards shifting the practice of one-size-fits-all AI to a more human-aware (human-centric), pluralist, and inclusive approach)“

I need to admit, I like this passage. We might never be able to create “one” centralized customers, universities, and science-friendly AI model. Instead, organizations will focus on achieving diversity, ethics, and fairness by creating artificial intelligence models, which, on one side, build strong, competitive advantage and, on the other, will serve their customers fairly.

Designing guidelines for responsible AI development is not a trivial task. Most of us have learned that Microsoft significantly reduced the workforce responsible for educating employees on the responsible development of AI tools (source). With this knowledge, it’s hard to talk about guidelines. Who should set them up? Who should be responsible for guarding them? Do we need AI law enforcement? Should those be incentives or penalties?

Conducting research for this article, I tried to stretch the extent and ensure I could find the answer on the business and government sides. Indeed, it was a good idea. Google published an exciting framework for building AI focused on responsibility (source). It recommends developing a solid experimental approach and monitoring the results. It leaves a lot of space for people behind building AI models. Can we be sure they will follow it and rigorously test models? I don’t know the answer. It’s easier to monitor and experiment with smaller data sets rather than bigger ones so the Google approach might be relevant to Soheil Human, Ryan Watkin’s suggestions.

The European Union is focused on a different approach; it’s more comprehensive, probably too comprehensive, and too general simultaneously (link). Interestingly, the EU’s way of developing artificial intelligence brings different layers, which makes AI responsible, while Google’s approach doesn’t mention them. Those layers are:

• Human agency and oversight,
• Technical robustness and safety,
• Privacy and Data Governance,
• Transparency,
• Diversity, non-discrimination, and fairness,
• Societal and environmental well-being,
• Accountability.

Maybe combining those two ways is a good thing, but not having staff responsible for responsible AI is not a solution. So, I guess Microsoft is not right!

The Collaborative Future of Human-AI Interaction

Harnessing AI’s power to augment human intelligence

I personally believe that the peak of productivity and business improvement can be achieved when artificial intelligence augments human work. My observation is based on what I see businesses doing while humans are on the hunt. Is putting products on the shelf a reputable and simple task?

Of course, your Cheerios box or Coca-Cola can are at the same place in the supermarket. Why not create robots to put those products on the shelf? Why are humans needed for that? Well, it looks like this task is not simple enough to be replaced by robots.

Four years ago, this happened when Walmart Inc announced the end of its partnership with Bossa Nova Robotics Inc, abandoning its years-long research and efforts to automate the task of scanning shelves and keeping track of inventory. This decision squashed the world’s largest retailer’s endeavor to improve customer experience and make shop staff jobs easier through the use of robots.

If we look at this figure and develop thinking around how artificial intelligence can help us, it’s easier to understand that AI needs us, and we need AI.

Source: Artificial intelligence, human intelligence, and hybrid intelligence based on mutual augmentation”, Jarrahi, Luts, Newlands

In practice, machine intelligence is often augmented by humans. AI’s narrow intelligence relies on extensive training data generated or processed by humans. Some AI vendors even conceal the human labor required for training AI services.

Furthermore, AI systems have the potential to augment human intelligence. Instead of replacing humans, AI typically extends or amplifies human capabilities by providing supportive tools like predictive analytics. Personal intelligent assistants, for instance, enhance users’ cognitive abilities by assisting in processing and navigating vast information landscapes. In strategic games like chess, grandmasters collaborate with AI to focus on strategic planning while relying on machines for analytical calculations. Augmented human intelligence emerges from the collaboration between humans and AI, enhancing and elevating human cognitive capacities.

Effective interactions between humans and AI lead to human-augmented AI and augmented human intelligence. The overlapping area between humans and AI represents their mutual progress through interaction.

Real-world applications of AI demonstrate that achieving intelligent performance goes beyond big data and algorithms; human contributions are crucial. AI systems are sociotechnical systems that can only advance by interacting and complementing human reasoning and machine decision-making.

Developing effective human-AI symbiosis requires addressing challenges like divergent human and machine tacit knowledge, which can hinder mutual understanding.

Examples of successful human-AI collaboration in different industries

Medicine and Health care

• The University of California, San Francisco (UCSF) developed an AI system to assist radiologists in analyzing mammograms. Example: UCSF’s AI system for mammogram analysis.
• AI technology aids doctors in various healthcare tasks and early disease detection. Example: AI-assisted surgery systems and disease detection tools.

Journalism:

• AI-powered tools like Grammarly and Hemingway assist with checking grammar and style errors.

Manufacturing:

• AI is used to analyze performance data and predict maintenance needs. Example: Airbus’s AI system for aircraft performance analysis. Hansrobot helps to increase manufacturing processes’ productivity.

Marketing:

• AI is used for personalized advertising, chatbots, and data analysis. Example: HubSpot uses AI to analyze customer data and optimize marketing efforts.

Law:

• AI is used for research and document analysis in the legal field. Example: Luminance is an AI-powered platform for legal contract analysis.

Agriculture:

• AI and machine learning analyze data to assist farmers in making informed decisions. Example: John Deere uses AI for soil analysis and crop planning.

Art:

• Generative AI is used to create original artwork. Examples: DALL-E, Jasper Art, Midjourney. Notable artwork: Portrait of Edmond de Belamy (an AI-generated painting sold for $432,500 at Christie’s in 2018).

How human-AI collaboration can shape a better society and world

The powerful combination of human creativity and advanced AI capabilities opens up a whole new world of possibilities for a human-centered society in the future, allowing us to achieve extraordinary feats that were once deemed impossible. AI is particularly adept at processing vast amounts of data, identifying intricate patterns, making accurate predictions, providing valuable insights into complex datasets, and uncovering hidden correlations that are difficult for humans to perceive.

However, humans bring irreplaceable qualities to the table, such as empathy, critical thinking, and nuanced decision-making skills, enabling us to navigate the nuances and ambiguities of our world. By thinking outside the box, empathizing with others, and applying ethical considerations, we can harness AI responsibly and beneficially for the betterment of society.

Together, humans and AI form an unbeatable alliance that has the potential to revolutionize various industries, from healthcare to finance to education to transportation, in a way that is focused on human needs and values. This partnership empowers us to solve complex problems, make informed decisions, and pave the way for a brighter future for all members of society. By embracing this transformative journey, we can achieve what was once considered unattainable and build a human-centered future.

Conclusion

In conclusion, AI has revolutionized numerous sectors by enhancing the precision of diagnostic predictions and enabling personalized approaches to many challenges humans and nature face. The achievements and advancements mentioned above were made possible through the collaborative efforts of individuals and artificial intelligence algorithms.

Recap of the collaborative potential of human-AI interaction

To a great extent, the collaborative potential of human-AI interaction answers the need for remarkable results. By combining human expertise with AI algorithms, significant advancements and improvements have been achieved in various fields. This partnership not only overcomes the limitations of each party but also provides a level of control over the outcomes. The collective intelligence of humans and the computational power of AI hold great promise for solving complex challenges, driving progress, and shaping the future.

The previous examples prove that people can effectively guide artificial intelligence. These instances demonstrate the successful collaboration between human expertise and AI algorithms, showcasing the capability to achieve desired outcomes. By leveraging the collective intelligence of humans and the computational power of AI, significant advancements and improvements have been realized across various fields. This reinforces the notion that human-AI interaction can lead to fruitful and impactful results when adequately guided.

Encouragement for a balanced approach that benefits both humans and AI systems

The key to unlocking the full potential of the relationship between humans and AI is to maintain a balanced approach that nurtures both. As technology evolves at an unprecedented pace, it’s crucial to prioritize the well-being of all parties involved.

By recognizing the strengths and capabilities of AI systems, we can use their power to solve complicated problems and enhance human abilities. At the same time, we must uphold human values, empathy, and ethical considerations in developing and deploying AI.

By promoting a harmonious coexistence between humans and AI systems, we can create a future where technology is a positive force for change, enriching our lives and enhancing our collective potential.

Let’s embrace a holistic perspective that values the well-being of humans and AI systems, paving the way for a world where innovation thrives and compassion guides our path forward.