Ins and Outs of Artificial Intelligence
Once considered a futuristic and inaccessible technology, artificial intelligence quickly ingrained itself into modern society to an extent not discernible to most people. Many of our daily experiences became streamlined (and monitored) by invisible algorithms without us even knowing it.
So how exactly does AI work? And should we be concerned with its growing role in our everyday lives?
What is AI?
Artificial intelligence essentially simulates human intelligence within a machine. Algorithms that programmers build into a computer reproduce traits typically associated with human logic. Examples include the ability to learn and adapt to large amounts of new data. This concept, known as machine learning, makes up the backbone of AI. Endless amounts of data get parsed by the computer in order to identify patterns and make predictions. The speed of these computations allows these computers to constantly absorb information and make accurate, real-time decisions.
Deep learning, a subset of machine learning, involves additional layers of computing and allows for the input of unstructured data. Accepting unprocessed data enables AI to be used in a wide variety of applications. As we will discuss later, these capabilities sustain applications such as virtual assistants and autonomous vehicles.
Types of AI
As computing power continues to expand since the advent of AI, the complexity and capabilities of these systems evolved. Once we had simple chess bots, now we possess tools present in our everyday lives.
Reactive Machines
The most basic form of artificial intelligence is known as a reactive machine. These computers have very specific purposes in mind when they are initially programmed. Consequently, the flexibility and reactivity associated with more complex systems are not required. As their names imply, reactive machines simply react to the situation in front of them without referring to past information or experiences.
A notable example of a reactive machine is IBM’s Deep Blue, a machine built to learn and play chess. In 1997, Deep Blue became the first computer system to defeat a reigning world champion in a chess match when it defeated Garry Kasparov in a six-game match. Chess machines are perfect examples of reactive machines because they are only concerned with the current state of the board and the potential moves that either player can make.
Limited Memory
Currently, limited memory AI is the category that is most visible to a typical consumer. These systems learn from previous experiences and apply these observations when considering any future actions. Consequently, this AI is capable of formulating more accurate predictions and completing more complex tasks. As we will discuss later, limited memory AI has been incorporated into many modern consumer electronics, such as virtual assistants and autonomous vehicles.
Theory of Mind
The next hypothetical stage of artificial intelligence is theory of mind AI. This form of AI is characterized by an understanding of human emotions and feelings. Having emotional intelligence allows systems to cooperate and communicate with humans more effectively, as well as make more accurate predictions regarding human behavior.
Developing theory of mind artificial intelligence would have a significant impact on the pervasiveness of computer technology throughout our society. For example, AI with these capabilities would likely be able to adopt caretaker roles in hospitals and assisted living facilities. Virtual assistants would be able to better adapt their behavior and predictions to their users, practically rendering human assistants obsolete.
Uses of AI
Voice Recognition
As previously mentioned, voice recognition software present in virtual assistants and voice transcription applications are powered by artificial intelligence. In 2020, it was reported that there were about 4.2 billion digital voice assistants in use worldwide. The most popular of these assistants include Apple Siri, Amazon Alexa, and Microsoft Cortana.
Users can utilize these assistants to complete a variety of tasks, such as creating calendar reminders, generating directions to a destination, and even simply just sending text messages. In order to do this, AI converts speech into text and processes this information in order to understand the request and context of this request. This process spots keywords and references grammatical rules that it learns in order to properly understand the user’s intent. Once the AI understands the query and formulates a solution, it then communicates this solution back to the user in their preferred language.
Advances in AI voice recognition capabilities enable higher quality transcription applications. One example of this functionality is transcribing meetings for team members who were unable to attend. Furthermore, AI can identify key words and phrases to create an effective summary of the entire meeting.
Voice biometrics are another potential use case for advanced voice recognition. This technology is especially impactful for businesses, especially in the realm of customer service. In the example of a bank customer service line, voice recognition software can authenticate users and allow them to perform low-risk operations without having to be transferred to a representative. The long sequences of automated authentications that are common when calling banks or Internet service providers would become obsolete as voice biometrics streamline the entire interaction.
Self-driving Vehicles
Present-day autonomous vehicles have come a long way in a relatively short amount of time. In 2004, the Defense Advanced Research Projects Agency presented a challenge for engineers to construct a fully autonomous car tasked with completing a route from Barstow, CA to Primm, NV. No team successfully completed this challenge. Today, cars such as the Tesla Model S are able to navigate roads and even park with minimal input from the driver.
The degree of autonomy varies depending on model and driving conditions. For example, BMW’s Extended Traffic Jam Assistant facilitates a mostly hands-free and pedal-free driving experience when the vehicle is moving at speeds less than 40 mph. In other cases, the vehicle provides less drive-assist features, but at higher speeds of travel.
To accomplish this, sensors monitor the position of nearby vehicles, pedestrians, lane markings, and obstacles. Additionally, the system keeps track of information from road signs and detects traffic lights. This data is constantly collected and fed into machine learning systems which then feeds instructions to the various vehicle control systems. Machine learning ensures that object recognition and predictive modeling are constantly improving and enhancing driver experience.
Facial Recognition
The ability to recognize faces is a prominent feature of AI technology that has already fully cemented its place in our society. Face ID, introduced by Apple in 2017, allows users to unlock their phone using facial biometrics. When given permission, this service can even be used to authenticate payments as well as log into financial apps and social media accounts.
Furthermore, as of iOS version 15.4 or later, Face ID is capable of verifying user identity even when wearing a mask. As expected, face biometrics are of particular use to law enforcement. Facial recognition is used to identify victims of human trafficking so that they can be located and recovered.
Generally speaking, there are three steps involved in the facial recognition process. First, the software must detect the face that is to be identified. Next, a “faceprint” is created by analyzing the face. Examples of this analysis include measuring the distance between facial features and noting their shapes. Lastly, the AI attempts to confirm the identity of the face in question. As the stored database of identities grows, facial recognition becomes faster and more accurate.
Risks of AI
“Unless we learn how to prepare for, and avoid, the potential risks, AI could be the worst event in the history of our civilization.”
-Stephen Hawking
As with any emerging technology, adoption requires weighing out the benefits against the risks. AI technology will only continue to become more ingrained in our daily lives, which brings a number of issues under close scrutiny.
Disinformation
Advances in machine learning technology have facilitated the efforts of organizations conducting misinformation campaigns. These organizations are able to refine the targets of their efforts by feeding huge amounts of data into AI and allowing it to create useful psychometric profiles and categories. This ensures that they are able to create content in a way that is perfectly customized to their audience.
The advent of deepfakes provides another example of an AI-powered tool used for disinformation. These realistic pictures and videos depict people saying and doing things that they did not actually say or do. Easy access to this very convincing and difficult-to-detect technology has dire implications for global political discourse. Compromising deepfakes of world leaders could have significant consequences, especially as they become increasingly convincing. On a smaller-scale, deepfakes could easily be used for blackmail, intimidation, and general smearing.
Privacy
As information continues to be constantly fed into AI systems and facial recognition capabilities improve, AI-powered surveillance systems will integrate as a common feature of many major cities. Consequently, government agencies will be able to monitor more aspects of their citizens’ lives. For many, this poses a serious privacy problem, especially in the case of authoritarian governments around the world.
Today, the introduction of China’s social credit system means that government surveillance has significant implications on the daily lives of Chinese citizens. In this system, a social credit score is assigned to each citizen, which is then added to or deducted from based on their behavior. Negative behavior includes not visiting elderly relatives, cheating in online video games, and playing loud music in public transportation systems.
Losing too many points may result in various punishments. Blacklisted individuals may have their pets confiscated, be denied employment in certain institutions, and banned from flights. The presence of advanced surveillance systems would obviously ensure that citizens conform to all standards set by the government.
Job Loss
Generally speaking, rapid advances in technology improve a society’s standard of living. But for many, the evolving capabilities of AI are perceived as a risk to their job security. Experts estimate that 40% of jobs in the next 15 years will be lost to automation.
Job displacement due to automation has occurred in the past, but the exponential rate of modern technological progression affects a significant segment of the population. For example, chatbots have already started to replace customer service representatives and self-driving cars are now replacing taxi drivers.
Autonomous Weapons
Although developing autonomous weapons systems theoretically remove warfighters from dangerous missions and reduces casualties, some argue that algorithms should not be tasked with deciding who lives and dies on a battlefield. Proper judgment of appropriate escalation of force as a function has always been perceived as uniquely human.
It is widely accepted that the ability to interpret body language and empathize with other humans cannot be replaced by a machine. Consequently, many experts believe that AI cannot be trusted with the capability to end a life. Furthermore, mobilizing AI to eliminate targets creates further disconnect between decision-makers and their targets, raising questions to how war should be conducted.
Building Responsible AI
The presence of these serious problems regarding AI implementation begs the question as to whether AI is even a viable solution for high-level tasks. So how are these concerns being addressed by researchers?
Avoiding Bias
Since AI builds its “knowledge” from data sets that are fed in by engineers, they are naturally prone to any biases within the data. These biases create significant repercussions in situations where the AI is relied upon to make decisions or selections. For example, cases of bias in school admissions and hiring algorithms regularly contribute to inequitable outcomes. Results like these create a sense of distrust, deterring the progress and adoption of AI in general.
As you may expect, a universally agreed-upon approach to achieving fairness in AI design does not exist. But, there are definitely general guidelines for improving impartiality in results. To begin, humans should not be completely eliminated from the decision-making process. This protects against obviously flawed results, allowing humans to double-check results or choose from recommendations.
Next, data collection techniques must improve. Higher quantities of data, along with transparent methods of data collection should ensure that input data is sourced more responsibly and has its bias diluted.
Managing Environmental Impact
Although AI often gets used in software that tackles the issue of climate change, the amount of computing power necessary for most high-level applications require a significant supply of energy to support it. Researchers predict that AI data centers could be responsible for up to 20% of global electricity use by 2030.
Fortunately, large tech firms have already started to address this problem. Microsoft and Google claim that their data centers emit zero to negative net carbon emissions as a result of the adoption of renewable energy. Hopefully, improvements in energy efficiency and an increase in awareness of the environmental concerns associated with AI computing indicate progress in managing environmental damage.
Transparency
In order to reassure users that AI is being used ethically and with reasonable objectives in mind, transparency in design and execution must be established when being implemented. The value of transparency increases as AI begins to impact more aspects of our daily lives. The ability to explain AI-made decisions enables human oversight, which helps protect against harmful and invasive AI. Additionally, as previously explained, incorporating humans in these decision-making processes helps avoid biased outcomes.
Conclusion
As we have discussed, endless possibilities exist for improved applications taking advantage of the computing power of artificial intelligence. But clearly, the risks associated with these benefits necessitate a high degree of diligence towards ethics and safety, both in design and implementation. As AI continues to assume more responsibilities in many workplaces, business leaders need to constantly search for ways to capitalize.
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