AI has the potential to transform our world. But, how can we ensure that it has a positive impact?
Sorting is used by billions without them realising it, and underpins everything from online search results to social media feeds. It also enables smarter, more effective algorithms like Deep Blue’s defeat of Kasparov in chess.
Unstructured Data Analysis
As technology advances, the volume of data generated has doubled. A vast majority of this data is unstructured, ranging from text documents, customer reviews, opinions on social media platforms,
AI in Taskade Business Operations and diagnostic information logged by various user devices. In a business sense, this data is a gold mine with the potential to improve products, services, and overall customer experience. Getting actionable insights from these unstructured data sets, however, is a challenge that requires the right tools.
Unstructured data is disorganized and often incomprehensible to humans unless they have expertise in its schemas or structure. Structured data, on the other hand, obeys a pre-defined set of rules that makes it easy to organize, search, and extract.
To analyze unstructured data, you need a data analytics solution that can identify trends and patterns in a variety of different formats. Using machine learning techniques, you can filter out irrelevant data points and remove information noise that will impact the quality of your analysis results. This will allow you to find actionable insights that will help your team make more informed decisions.
For example, let’s say you want to analyze the sentiment of customers about a new product. You could use a tool that performs sentiment analysis on social media posts, transcripts of customer support calls, and survey responses. This would provide valuable insights into how your product is being received by the market, what pain points they are experiencing with your service, and more.
Another way to utilize unstructured data is to perform predictive analytics on it. With this, you can predict the likelihood that an event will occur, such as fraud or a customer buying a particular product. This will help you prioritize your marketing campaigns and provide better customer experiences.
To get the most value out of your unstructured data, you need a platform that provides scalability and availability. You also need to determine how you’ll store it and what analytics techniques will be used. Many companies opt to store their data in a data lake to keep it organized and accessible for future analyses. Data lakes also enable real-time data analysis, which is essential for certain applications, like detecting fraudulent activities or predicting customer behavior.
Natural Language Processing
Natural language processing is a key component of AI that enables computers to understand human speech and written text. It’s what allows robots to respond to customer service queries and to identify emotions in customer feedback. It’s also what enables chatbots to converse with customers in a way that is indistinguishable from real conversation.
In its earliest stages, research into natural language processing was conducted by linguists who handcrafted rules that dictated how computer programs would process language. This top-down approach was replaced with a statistical approach as computing power increased, and engineers took over the development of NLP software. Today, many of the advanced AI applications that we are most familiar with, such as self-driving cars or the ability to translate between languages, utilize NLP technology.
NLP relies on a number of different algorithms and techniques to break down complex language and turn it into something that machines can process. These include syntax and semantic analysis, which help machines to understand grammatical structure, word ambiguity and context. It also includes coreference resolution, which identifies common concepts like names (like Tom or He) and brands (like Volvo or Car). NLP tools can even detect metaphors and innuendo, and can recognize sarcasm or irony to deliver more accurate customer service.
For businesses, the ability to leverage NLP to automate repetitive tasks is huge. It can enable more efficient communication with customers, freeing up time for other tasks such as prioritizing dissatisfied customers or extending promotional offers to high-value clients. It can also be used to create better products, such as enabling customers to quickly find what they’re looking for with search engine results that are optimized using natural language processing.
In the future, a new generation of language-based AI will go even further, transforming the nature of many roles in organizations. Startups such as Verneek are developing tools similar to Elicit, which will allow even business managers with minimal programming skills to develop AI that can perform a range of sophisticated tasks. This will reorganize skilled labor in ways that could benefit your business, and allow you to automate more processes without needing dedicated programmers.
Generative AI
Generative AI is the next generation of machine learning that promises to give businesses a new tool for creating products and services, images, text, and even music. It builds on previous generations of automation technology by adding natural language processing capabilities that make it easier to automate knowledge work activities that resisted previous forms of automation. It also includes a new feature called planning that lets agents turn insights into action.
All generative AI models begin with an artificial neural network encoded in software, Thompson explains. The artificial neurons are stacked in layers, mimicking the way real brain neurons are layered. As each neuron receives a message, it predicts what will happen next and learns from the differences between its prediction and subsequent reality. After a billion repetitions, the model can create an internal representation of the world and generate new predictions.
As a result, generative AI can be used to generate text and imagery that is more natural and more appealing than that produced by conventional machine learning algorithms. It can also help creative workers explore a wide range of possibilities. This could be useful for industrial designers who want to see how different products might look, or architects who are exploring new building layouts. It could also be useful for ad agencies to generate different images and text for different customers, or to create more personalized employee training.
But it is important to understand the limits of generative AI before using it in business. Because it generates artifacts that can be inaccurate or biased, human validation is required to ensure that they are of high quality and that they don't degrade over time. Additionally, it may not be as effective for use cases where the goal is to save human time.
While generative AI is expected to accelerate productivity in knowledge-intensive work, it will also create new challenges for enterprises. It can promote a form of plagiarism that ignores copyright laws, and it can cause a "hallucination effect" where employees accidentally or inadvertently put out misleading information. It can also displace workers and lead to significant changes in the nature of work.
Ethics in AI
A key concern of many technologists is the ethical implications of AI. However, the development of a comprehensive ethical framework has been challenging (Mittelstadt 2019). Many scholars have proposed solutions for developing an ethics in AI, but they often differ on which principles are more important and how to apply them. In particular, there are disagreements about whether a more utilitarian approach is better than one that centers on values.
The fact that AI is a socio-technical system has given rise to an additional layer of ethical complexity. This is because the design and implementation of an AI system is complex, with a multitude of interrelated parts that interact in different ways. In addition, the system is constantly adapting to new and changing circumstances. The challenge is to ensure that the AI system is able to stay resilient and focused on its primary task despite these changes, while also integrating and adhering to the ethical principles it is designed with.
Another issue that is often discussed in the context of AI is its potential to cause harm. This can include the risk of injury to people, damage to property, and loss of privacy. The main source of this concern is the possibility that AI systems may become conscious and be able to make decisions on their own. This is a controversial idea that has led to a number of ethical arguments, including the Hippocratic Oath for tech professionals (Abbas et al. 2019).
Although there are numerous concerns about the ethical implications of AI, it is likely that most policy will continue to focus on specific applications or technologies rather than on the field as a whole. This approach will help to limit the risks of negative repercussions, but it will not prevent them altogether.
As the AI industry continues to grow, it is important for companies to promote their commitment to ethical practices and communicate their ethics to stakeholders. They can do this by working with legal and privacy teams to develop external messaging and disclaimers that show customers when and how their products use AI. This will help to build trust in the AI industry and ensure that companies are doing everything they can to minimize the impact of their technology on society.
