Designing software for businesses and consumers is a data-intensive process that requires in-depth knowledge of user preferences and expectations. Users often don’t know what they’re looking for until it’s presented to them by a software manufacturer. One way software makers can gain insight into market trends is through artificial intelligence and data science analytics. Large-scale trends are often observable at a level that is too complex to observe in the usage habits of individual users.
AI and Data Science Applications for Software
By using Artificial Intelligence (AI) techniques known as machine learning and deep learning, software engineers aren’t giving rules for how a computer will make decisions or decide actions. Instead, they are tailoring the data by using domain-specific functions, which are fed into learning programs. These programs are iteratively or continuously improved as more information is offered to the computer.
The significance of this is that the computer is learning about patterns or features that are important without the engineer’s intercession. However, to make the most use of this new feature, it is helpful if the engineer is also fluent in the social science of data gathering. To create effective and continuously productive programming, from which the computer learns and can produce startling, novel, and useful patterns, individuals with multiple fields of knowledge at their avail are crucial.
With the introduction of machine learning and deep learning, this fundamentally changes the roles of software engineers. Instead of managing large repertoires of software or write intricate programming, they will be responsible for data—the cleaning, collating, labeling, and analyzing of it. This means that the fields, while quite distinct, will be brought closer together as computers gain in their proficiency. The neural network isn’t simply a tool, it is becoming the framework itself and re-establishing how software is designed.
This resolves one large problem area. Instead of a full-stack developer designing a program from scratch, which must be constantly updated and integrated with other programs through human means, leading to unresolvable mistakes, known as bugs, the AI maintains a neural net. These are patterns and important characteristics that show up in data that are “remembered.” While these neural nets have their own issues and maintenance requirements, such as retraining an algorithm with more data or updated information, they are significantly easier to manage.
While the types of AI represented by machine learning and deep learning have many benefits, such as enhanced portability, the ability to integrate it easily into hardware, constant runtime and learning curves in which the AI adds to its knowledge base, and other aspects of technology, there are some drawbacks. First, human incapacity to understand the finer points of how machine learning actually works leads to programmers treating these programs as a sort of black box. Since they do not understand how data is processed or how important patterns are distinguished, they simply let it run and assume its product will be good.
This leads to the second human flaw, which is programmer bias in designing the parameters the AI uses to enact a learning program. Bigoted bots are a direct result of unexamined assumptions made by the programmers, and certain cultural patterns that are assumed to be natural. This leads to skewed data presented by the AI, which must then be scrapped and purged, but usually not before results are made public and flaws pointed out by individuals from other cultural backgrounds. This is the rationale for the next type of career, which could be said to come at AI and the science of big data from another direction.
AI and Data Science in Anthropology
Anthropologist Clifford Geertz coined a term that is exceptionally and unexpectedly useful for those working with AI and data science. Thick description is considered de rigeur is anthropology, but is only coming into use in tech circles, where it is most needed. This is because these particular pursuits are interested in understanding patterns related to human behavior, factors that affect it, and cultural impediments or easements that may impact these behaviors and facets of human life. Until recently, machine learning about humans operated in a void in which culture was not considered by programmers.
However, now those who deal with human data—social scientists, anthropologists, and others—are now being leveraged to shape the way data is collected and prioritized. This is largely because anthropologists can be taught aspects of the field of machine learning and data science but the reverse is not always the case. There is a unique perspective that comes with the social science mindset that simply isn’t an aspect of the harder sciences and those disciplines that deal with computers.
This is why more anthropologists should consider applying their understanding to the field of data science and AI. Because their unique vantage point helps to shape machine learning to be effective. Aspects such as how people behave in groups, how they behave in the privacy of their own homes and singly, and the choices they make from those that are presented to them by the culture in which they live are important factors when considering data.
Even the gathering of large data sets may be informed by anthropologists and social scientists. They consider factors such as ethnicity and how it shapes word view. As well, they may consider how different cultures interact within the same society, and how constraints that would be less visible to a computer scientist may come into play when designing the parameters of a program. Data without context is less effective and less accurate.
But to teach individuals who do not think in terms of people, culture, and human adaptation that these things are important is a difficult task. It’s better to inform AI with ethics and context by teaching human scientists to think about computers. This is why, when obtaining the first stage of higher education, students of computer science should take courses that also deal with sociology or anthropology. This will encourage them to see problems and questions in a different light and influence their programing style.
While the past thirty or forty years of the Internet revolution has, understandably, been technology-led, what comes next and is in the processes of forming now must be more tailored to the customer. This customer-centered view is impossible if the parameters designed by computer scientists and by which the AI gathers and analyzes data do not consider various types of customers who have different needs, preferences, and backgrounds. For example, machine learning about patterns in retail must take the customer bases into account in order to provide accurate forecasts. This leads back to the data set, which is then parsed further by the AI. Inaccurate predictions can cost companies large amounts of money at the very least, and at most can damage their brand.
The health care industry is a rapidly growing field that requires a great deal of information analysis and processing. Artificial intelligence is helping data professionals in the health care industry find diagnoses and treatments for conditions that were previously difficult or impossible for trained doctors to discover on their own. Not only do artificially intelligent robots perform medical procedures such as surgeries and minor operations, but algorithms powered by machine learning will soon be able to provide psychiatric therapy for patients, according to Forbes.
Bioinformatics and AI have a long history together. However, lately machine learning algorithms have instigated a renaissance in this relationship. It is not enough to have a batch of unprocessed data, which has relatively little meaning without the context of information and knowledge. This is where data scientists come into the picture, by assigning parameters to the data that allow AI to apply context to raw data and interpret patterns within it.
The field rests on certain key processes, which goes beyond the data acquisition and interpretation. Public health statistics, for example, begin with the data, but patterns related to ethnicity, socioeconomic status, and other factors are soon apparent if the appropriate parameters are set. Without them, the data does not make sense or it paints a very different picture. In order for information to be an entity, it must be open to interpretation, mining, retrieval, the communication of aspects or features, and, above all, stored in a safe and accessible location, which is often in a cloud server. Then, large-scale predictive models can be applied to it.
The banking industry is a data-rich field with a strong need for analytics processes powered by AI. With advanced machine learning algorithms and data science practices, investment bankers can predict long-term financial trends with much better accuracy than was previously possible. Many trades are possible only with the help of artificial intelligence because they must be executed in the span of a few milliseconds. Data science helps financial brokers make decisions for clients and the companies they work for.
By understanding both data science and AI, with a healthy dose of social science, parameters for these algorithms can be increasingly tailored. Because the stock market runs on human emotion, responding almost presciently to a major election or a natural disaster, AI engaged in deep learning must be responsive to such factors. While it can enact a trade in a fraction of the time it requires human agents to do so, making proper decisions is important. If the data gathering has not been tailored by individuals who understand more than the physical and rational aspects of the market, the margin for error becomes much tighter.
As intelligence agencies monitor online groups, foreign governments and dissident movements, they use data science and artificial intelligence to detect patterns and trends in the behavior of their subjects. From private investigators to government agencies, intelligence gathering is an important job that requires huge amounts of data and insightful observations about its meaning. Intelligence agencies and private investigators can use data science to monitor websites and servers connected to political extremists, foreign adversaries, and political campaigns.
Since AI is capable of combing through enormous data sets and picking out patterns, it’s become increasingly useful for large agencies or the military to have entire departments of data scientists with specialties in deep learning and machine learning. Neural nets are formed over time, and patterns that were useful in the past are stored for future application. As new threats emerge, AI programs are given more parameters to search and ever more data. Data scientists then assess patterns and tailor the algorithms, correcting for any errors.
While it’s illegal for government agencies to intervene in the political process, it’s not illegal for private firms to conduct opposition research for a political candidate. It’s illegal for the government to use political opposition research to grant warrants for surveillance, but it’s legal for media outlets and politicians to publicize information obtained through data gathering and machine learning. Data science and machine learning can be used to micro-target voters based on issues that matter to them, as the Obama campaign did during the 2012 election.
What is also apparent is that, with the entrenchment of social media in the lives of many, this technique can be put to use by private organizations from around the world to influence the voters of a specific nation. This particular use of AI and data science takes on the tone of a national security threat when misinformation is intentionally used to sway public opinion. Hence, although this touches upon intelligence gathering and the larger community engaged in it, what it also means is that there is a need for individuals who understand both data science and AI in order to combat the microtargeting of specific groups with such misleading information and for those individuals to use their knowledge to combat it.
With data science becoming increasingly important in the economy, many careers rely on analytics and machine learning to gather information and make predictions. Careers that combine AI with data science include a wide range of economic industries and professions.