Artificial Intelligence.pptx
- 2. Artificial intelligence ⢠According to (burns, 2021) Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. How does AI work? ⢠As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. ⢠No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.
- 3. ContiâŚâŚ ⢠In general, AI systems work by ingesting large amounts of labelled training data, analysing the data for correlations and patterns, and using these patterns to make predictions about future states. ⢠In this way, a Chabot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples. ⢠AI programming focuses on three cognitive skills: learning, reasoning and self- correction. Learning processes. â˘This aspect of AI programming focuses on acquiring data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
- 4. ContiâŚ. Reasoning processes ⢠This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome. Self-correction processes. ⢠This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible. Why is artificial intelligence important? ⢠(burns, 2021) says AI is important because it can give enterprises insights into their operations that they may not have been aware of previously and because, in some cases, AI can perform tasks better than humans. ⢠Particularly when it comes to repetitive, detail-oriented tasks like analysing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors.
- 5. Advantages ⢠Good at detail-oriented jobs; ⢠Reduced time for data-heavy tasks; ⢠Delivers consistent results; and ⢠AI-powered virtual agents are always available. Disadvantages ⢠Expensive; ⢠Requires deep technical expertise; ⢠Limited supply of qualified workers to build AI tools; ⢠Only knows what it's been shown ⢠Lack of ability to generalize from one task to another.
- 6. Strong AI vs. weak AI AI can be categorized as either weak or strong. ⢠((AI), 2020) states that there is Weak AI, also known as narrow AI, is an AI system that is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple's Siri, use weak AI. ⢠Strong AI, also known as artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain. When presented with an unfamiliar task, a strong AI system can use fuzzy logic to apply knowledge from one domain to another and find a solution autonomously. In theory, a strong AI program should be able to pass both a Turing Test and the Chinese room test.
- 7. What are the 4 types of artificial intelligence? ⢠Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, explained in a 2016 article that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to sentient systems, which do not yet exist. The categories are as follows: Type 1:Reactive machines. ⢠These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones. ⢠Type 2: Limited memory. ⢠these AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way. Type 3: Theory of mind. ⢠Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behaviour, a necessary skill for AI systems to become integral members of human teams. Type 4: Self-awareness ⢠In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
- 8. Examples of AI technology and how is it used today? Automation. ⢠When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. ⢠When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA's tactical bots to pass along intelligence from AI and respond to process changes. Machine learning. ⢠This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:
- 9. ContiâŚ. ⢠Supervised learning. Data sets are labelled so that patterns can be detected and used to label new data sets. ⢠Unsupervised learning. Data sets aren't labelled and are sorted according to similarities or differences. ⢠Reinforcement learning. Data sets aren't labelled but, after performing an action or several actions, the AI system is given feedback. Machine vision. ⢠This technology gives a machine the ability to see. Machine vision captures and analyses visual information using a camera, analogy-to-digital conversion and digital signal processing. ⢠It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.
- 10. ContiâŚ. Natural language processing (NLP). ⢠This is the processing of human language by a computer program. One of the older and best- known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition. Robotics. ⢠This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. ⢠For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings. Self-driving cars. ⢠Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.
- 11. How to leverage AI in finance and accounting ⢠Auditability ⢠Data analytics establishes the scope of the audit. Cognitive computing, predictive analytics, and AI enable tracking more complex transactions that go with estimates and judgments ⢠Chatbot Support ⢠AI-driven chatbots help solve user queries quickly and efficiently, including queries on account balance, financial statements, account status, etc. Tracking outstanding invoices and automating the follow-up collection processes with AI ensures that accounts are kept balanced and closed promptly. Moreover, AI chatbots answer customers' routine questions and can provide level-1 support. ⢠Payables/ Receivables Processing ⢠Invoice processing is considered time-consuming and labor-intensive parts of the enterprise. AI-based invoice management systems help by increasing the volume, performing zero-error processing, and improving vendor relationships ⢠Supplier Onboarding ⢠The AI-based approach helps expand customers' reach, increase revenue, and evaluate the suppliers with minimal human intervention
- 12. Conti⌠⢠Procurement Processes ⢠Purchasing and procurement processes mean a lot of paperwork - sometimes in different systems that are seemingly unconnected! With AI-driven workflows, finance teams can process unstructured data while automatically mitigating governance/compliance/risks. ⢠Auditability ⢠Data analytics establishes the scope of the audit, and risk assessment as RPA and analytics facilitate tracking of routine transactions. Cognitive computing, predictive analytics, and AI enable tracking more complex transactions that go with estimates and judgments. ⢠Monthly and Quarterly Cash Flows ⢠AI-based tools empower enterprises to reconcile financial activities quickly, understand historical cash flows, and predict future cash requirements. AI applications also ensure that all financial processes are secure by collecting data from many sources and integrating the data. ⢠Expenses Management ⢠When manually done, managing expenses-related processes is not only filled with complex paperwork - but also prone to fraud and data breaches. Expenses management automation ensures almost zero errors and alerts the team to a breach if it occurs.
- 13. Role of AI in finance and accounting ⢠New technology is shaping Industry 4.0 in every vertical with intelligent responses to changing expectations of customers, suppliers, vendors, and partners. Automation enables a reduction of 80-90% of the time previously taken by the workforce in performing disparate and repetitive tasks manually. It also enhances the quality of the output by reducing human error. ⢠Almost all accounting tasks, including payroll, tax, banking, and audits, have become automated with AI, disrupting the accounting industry, and bringing about a big change in how business is done. ⢠AI boosts productivity and output quality even as it results in greater transparency and auditability. ⢠AI provides a broad range of opportunities and minimizes the traditional time-consuming responsibilities of the finance team to look at more venues for business growth. ⢠AI helps in forecasting accurate financial statements. With machine learning (ML), finance professionals can predict future trends based on historical data/ records. ⢠Robotic Process Automation (RPA) can complete repetitive tasks in the enterprise's business processes with amazing efficiency, including document or data analysis. With RPA in place, the finance team can break free of getting bogged down by non-value-added tasks. Instead, they can focus more on taking up strategic and advisory responsibilities.
- 14. Letâs look at a few applications of RPA and Intelligent Automation in accounting: ⢠AI processes documents in real-time using natural language processing and computer vision to generate reports also in real-time. Such reporting provides insight, ensuring that the enterprise can be proactive and change course if necessary. ⢠AI enables the processing and automated authorization of documents to enhance internal accounting processes such as procurement and purchasing, invoicing, purchase orders, expense reports, accounts payable and receivables, etc. ⢠AI-enabled systems support auditing and compliance with corporate, state, and federal regulations by monitoring the pertinent documents and raising alerts where necessary. ⢠ML algorithms sift through voluminous data, identify potential fraud issues, and flag them for review to avoid loss of revenue.
- 15. How artificial intelligence will impact accounting ⢠But as a recent report from Deloitte highlighted, technology advances have historically eliminated some jobs and created others. Thereâs no reason to suppose that this trend will not continue, says Deloitte: âWe cannot forecast the jobs of the future, but we believe that jobs will continue to be created, enhanced and destroyed much as they have in the last 150 years.â ⢠âWill AI reduce the need for accountants? I think the answer is probably yes,â says Richard Anning, head of ICAEWâs IT Faculty. âBut you have to define what an accountant is. If youâre looking at some of the more repetitive bookkeeping or process-driven tasks, those are more likely to be subject to automation than the higher value tasks,â he says. ⢠Michael Whitmire, CEO and co-founder of FloQast, an accountancy software start up based in Los Angeles, agrees: âAccounting departments overall will be trimmed down and the employees left will be able to focus on more strategic initiatives, like process improvement, cost control, and capital optimisation. AI is already beginning to automate tedious tasks such as data entry. Automation is occurring at the staff level, but it will creep up the corporate ladder and begin to automate higher level accounting jobs,â he says.
- 16. Inherent challenges of adopting AI in accounting ⢠No doubt, the stakeholders of an enterprise have recognized the value of adopting AI-powered systems and applications. However, it also requires a shift in the mindset of more than the CFOs. The finance and accounting professionals need to make the shift and equip themselves with the necessary skills and knowledge. ⢠The finance teams must appreciate that they are now free to contribute to new business relationships, improve existing partnerships, and work from a position of strength, thanks largely to AI and the resulting critical insights. ⢠Enterprises must not only invest in technology but also the workforce required to handle said technology. It means that they must also provide proper training and support for the teams to use AI to optimize productivity efficiently. ⢠As advanced technology evolves, it will only become more sophisticated, with more tools and systems becoming available to finance and accounting. The rapid expansion into digital transformation with AI and automation sets the pace for quick learning and the adoption of new ways of time and cost-cutting. ⢠Finally, finance and accounting teams adopting AI to their practice will be better able to analyse a tremendous amount of data, identify patterns and trends. Even better, they will be able to use the latest technology and tools to support various working modes and geographies by taking over routine tasks that are better suited to machines.
- 17. THANK YOU References (AI), A. I. (2020). Artificial Intelligence (AI). IBM Cloude Hub, 1. ACIEW. (2003). How artificial intelligence will impact accounting. ACIEW, 2-7. burns, e. (2021). What is artificial intelligence (AI)? tech target , 1. Members 1. Ruvarashe Mawoko 181430 2. Kundai Chigodora 181413 3. Rumbidzai Munyanyi 181622 4. Charmaine Madzima 181971 5. Victoria Chagwiza 181611