How to Identify Business Opportunities with AI Essay Sample
AI is essential, transformational automation like the microprocessor or the worldwide web. In simple terms, as defined by Kevin, Co-founder Prelego, in an E-Seminar on how to identify business opportunities with AI, is intelligent computers (2022). The E-Seminar aimed to learn how AI may produce money once employed in commerce, how to discover and build AI use cases inside organizations, and how to avoid risks associated with any new AI endeavor. This essay will cover a summary of the seminar, main lessons learned, future directions and suggestions, and finally, touch on some missing points and disagreements.
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The speaker begins the seminar by expounding more on the basics of AI. Machine learning and deep learning are the two fundamentals of AI. Machine learning is a sort of AI and a distinct approach to software development. The difference between traditional programming and machine learning is that, in conventional programming, a programmer explicitly tells a computer what to do (2022). In contrast, in machine learning, you take examples or training data, feed it into an algorithm, and train it to know how to make predictions. On the other hand, Deep learning is a sort of machine learning at the cutting edge of AI. Deep learning is unique in that it has made fundamental breakthroughs in the most challenging computer issues and excels at handling complexity.
There are three main principles for an effective and efficient AI strategy. The first is to determine an output when purchasing a machine-learning product. Examples of production may be estimating future costs or resources to categorizing a document, among other things. Second, determine whether you have training data (2022). Some inputs are predictive of output, such as historical sales data. Finally, choose a model or algorithm, a piece of code that you can train with your data to make predictions. Examples of models are multi-layer perception for predicting sales and random forest for detecting fraud.
Kevin discusses four AI product patterns that can be employed to spot potential start-up opportunities for a company to identify the AI opportunities. One of the patterns in computer vision is used to discern what is in an image. It is used in biometrics, recovering a specific picture from a big file, and detecting an item in an image, among other things (2022). The second pattern is Natural language processing (NLP), a computational approach interacting with natural language. Speech recognition and text production, as well as summarizing and classification, are some examples of applications. NLP is applicable in various situations, such as finding the most exciting material in an email or detecting whether a document breaches any compliance regulations.
The next-in-sequence pattern uses structured data such as sales marketing tables, online user activity, server logs, and time-stamped events to make predictions. Determining client KPIs and seeking new data to enhance existing processes are two examples of next-in-sequence business opportunities. The final pattern is the Collaborative filter utilized when there are many people and things to make a recommendation. Collaborative filters are applicable in content recommendations such as Netflix queues and might be used in E-commerce product recommendations. The major challenge of the developed patterns is coming up with training data, and it might require redefining the problem or in computer vision hand labeling.
The main lessons learned from the E-Seminar major in how to apply basic AI concepts: Like training data, machine learning, and deep learning in customers’ quotes and business plans, as well as how to detect future trading ventures for AI products like computer vision and natural language processing. The correlation between training data, machine learning, and deep understanding improves the efficiency and time required to conduct business activities, resulting in a competitive advantage for the user organization. Another lesson is that combining image (computer vision), text (NLP), and table (next in sequence) in a model can help build networks that are more complex and generate a more efficient output leading to business success. Technology may have a considerable impact when applied to typical business challenges.
Artificial Intelligence (AI) has progressed from being a science fiction or academic research interest to a point where it is set to influence human lives significantly. Autonomous vehicles, medical diagnostics, and conversational bots are becoming a reality, among other AI-driven applications. Without a backup human driver, self-driving automobiles are on the road. AI-based medical diagnosis software outperforms qualified doctors (Gulshan et al., 2016); Conversational agents are becoming increasingly common in many aspects of life (Venkatesh et al., 2018). AI-enabled bots defeat world champions [Silver et al., 2017].
While AI has made significant technological gains, specific fundamental difficulties need to be addressed systematically, mainly because it is on the verge of becoming a reality. The following are some of the issues: explainability AI-enabled systems must be able to explain their conclusions, such as On what grounds was a patient diagnosed with a malignant disease? Users’ confidence would undoubtedly grow if they could present rational and credible proof for their actions (Mannarswamy & Roy, 2018). Secondly, fragility When placed in a different but similar context, AI and DL systems in diverse situations have been documented to behave in unexpected and undesired ways. Minor changes in the input data, such as one pixel in an image, might result in drastically different outputs, which could have fatal consequences for real-world applications relying on deep computer vision models.
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