The global energy industry is facing fundamental shifts in the way it generates, sells and distributes power. The pressure is on to cut carbon emissions and, as a result, methods must be found to manage the increasing gigawatts of unpredictable, weather-dependent renewable energy flowing on to power grids. By using Artificial Intelligence we can create forecasts for electricity demand, for generation and weather by lessen the need for these backup mechanisms by predicting and managing fluctuations in production.
AI research is investigating decision-making with a scale and complexity that begin to exceed that manageable by a human operator. Ceding control of your home to a remote AI might seem like the stuff of science fiction, but the integration of AI into our appliances is already underway. For example, AI is being used to manage energy use in a device most of us use every day — mobile phones. The latest iteration of Google’s Android phone operating system includes a function which studies your app habits to ensure battery is deployed only on the ones you like the most.
The use of AI enables the consumer to have foresight over their energy profile for the first time. AI can now also work out how much electricity each of your home appliances is using, too. Home appliance manufacturers will come under increasing pressure to produce energy-efficient products. With access to exactly what it costs to run a dishwasher or TV, consumers could rapidly become disenchanted with power-high devices. AI and energy will be about reshaping the relationship between consumer and supplier.
Auditors must have a refined skill set to be successful. Much of the training and education surrounding auditing focuses on the mechanics of auditing. One of the best examples of Artificial Intelligence put to use in audit accounting is in the review of high volumes of texts such as the contracts.
Natural language processing (NLP) is a part of the artificial intelligence domain focused on communication between humans and computers. NLP attempts to address the inherent problem that while human communications are often ambiguous and imprecise, computers require unambiguous and precise messages to enable understanding.
The accounting, auditing and finance domains frequently put forth textual documents intended to communicate a wide variety of messages, including, but not limited to, corporate financial performance, management's assessment of current and future firm performance, analysts’ assessments of firm performance, domain standards and regulations as well as evidence of compliance with relevant standards and regulations.
Natural language processing is used to mine these documents to obtain insights, make inferences and to create additional methodologies and artefacts to advance knowledge in accounting, auditing and finance.
Forensic auditing tech in a digital future is analogous to Moore’s Law, named for Intel co-founder Gordon Moore. Just as he contended that integrated circuit power would double every two years, new accounting technologies are developing much more quickly today than ever before.
The real-time insights into areas of heightened risk and in internal controls as one of the most important benefits of advanced technologies in financial reporting. To that end, the continuing advancement of audit data analytics will play a major role. As opposed to working with a sampling of transactions, data analytics can examine 100% of transactions at a speed and pace unimaginable two decades ago.
While many businesses more clearly understand the need to be ready for a cybersecurity breach, an internal threat or fraud demands an equal level of preparation. It is vital that organizations approach the challenges they face in a disciplined way by understanding the protection choices they have and deploying the right solutions in an orderly manner. Mission-critical items range from vetting lawyers and forensic accountants to make sure they have no conflicts of interest, to setting up contracts for fair pricing in an efficient, non-stressful situation before fraud strikes.
Why is it so important to care for these details in advance? In the event of a breaking case of fraud, businesses can move on an action plan and not lose any time in those first few hours, when evidence can be compromised. Advance planning can mean the difference between confiscating a laptop right away, or losing valuable time and evidence while thorny details are worked out.
Meanwhile, forensic auditors will want to bolster their own preparation by doubling down on education. Just a generation ago, much of the auditor’s work was done with phones and fax machines. Today, between 24/7 connectivity, mobile technology, big data and massive increases in speed, “slow-down time” is a must. Auditors need to step outside the workday flow and devote time to learning about the latest industry developments through conferences, courses and online research. Advances in auditing that once took decades to unfold have been compressed into months—and sometimes, weeks.
Using AI tools, upstream oil and gas companies can shift their approach from production at all costs to producing in context. They will need to do profit and loss management at the well level to optimize the production cost per barrel. To do this, they must integrate all aspects of production management, collect the data for analysis and forecasting, and leverage artificial intelligence to optimize operations.
On a macro scale, deep machine learning can help increase awareness of macroeconomic trends to drive investment decisions in exploration and production. Economic conditions and even weather patterns can be considered to determine where investments should take place as well as intensity of production.
For example drilling is an expensive and risky investment, and applying AI in the operational planning and execution stages can significantly improve well planning, real-time drilling optimization, frictional drag estimation, and well cleaning predictions.
Today, AI systems form the backbone of digital oil field concepts and implementations. However, there is still great potential for new ways to optimize field development and production costs, prolong field life, and increase the recovery factor.
The promise of AI is already being realized in the oil and gas industry. Early adopters are taking advantage of their position to get a head start on the competition and protect their assets. The industry has always leveraged technology to adapt to change, and early adopters have always benefited the most. As competition in the oil and gas industry continues to heat up, companies cannot afford to be left behind. For those that understand and seize the opportunities inherent in adopting machine learning technologies, the future looks bright.
The main objective is to reduce the large amounts spent on new players, as well as the many lending.
According to Reuters, which had access to the report delivered to the leadership of the world football federation, there is a proposal to adopt a machine learning algorithm. The main objective is to reduce the overpayments (especially) spent on young footballers.
At the same time, it is envisaged to impose a special "luxury tax" on those who spend too much money on their aid and to allocate money to a charitable fund. Also, it is sought to control the number of players granted and obtained in the form of borrowing.
The first thought is that a team can borrow six to eight players per season, three more than the same origin group. That's because a team - whose name has not leaked - seems to lent 146 Players from 2011 to 2017.
The current system has led to excessive spending on young players and violations of the integrity of the events, resulting in bad practices that may lead to exploitation of the players.
The transfer system has been turned into a speculative market. This is not fair for football clubs or amateur clubs that form the basis of professional sport. The new machine learning mechanisms operate in the direction of transparency and objectivity, the report said.
At the core of this impact are the advancements of artificial intelligence, machine learning, and deep learning.
These change agents are ushering in a revolution that will fundamentally alter the way we live, work, and communicate akin to the industrial revolution – more specifically, AI is the new industrial revolution.
The most exciting and promising of these frontier technologies is the advancements happening in the deep learning space.
Deep Learning is an amazing tool that is helping numerous groups create exciting AI applications. It is helping us build self-driving cars, accurate speech recognition, computers that can understand images, forecasting methods for sales and marketing, driving advancements in healthcare, creating efficiencies in the power grid, improving agricultural yields, and helping us find solutions to climate change.
A handful of high-profile experiments came into the spotlight, including Microsoft Tay, Google’s DeepMind AlphaGo, and Facebook M and highlight the versatility of deep learning and the application of AI.
For instance, Google DeepMind has been used to master the game of Go, cut their data center energy bills by reducing power consumption by 15%, and even working with NHS to fight blindness. These experiments all rely on a technique known as deep learning, which attempts to mimic the layers of neurons in the brain’s neocortex.
Deep learning is a subset of a subset of artificial intelligence, which encompasses most logic and rule-based systems designed to solve problems. Within AI, you have machine learning, which uses a suite of algorithms to go through data to make and improve the decision making process. And, within machine learning you come to deep learning, which can make sense of data using multiple layers of abstraction.
During the training process, a deep neural network learns to discover useful patterns in the digital representation of data, like sounds and images. In particular, this is why we’re seeing more advancements for image recognition, machine translation, and natural language processing come from deep learning.
One example of deep learning in the wild is how Facebook can automatically organize photos, identify faces, and suggest which friends to tag. Or, how Google can programmatically translate 103 languages with extreme accuracy. In the next decade, AI will transform society. It will change what we do vs what we get computers to do for us.
As Artificial intelligence is gaining importance in the field of business travel, many travel management companies use automation technology to make the complex tasks simpler. Besides enhancing the travel experience, AI can also be immensely useful in improving travel and expense compliance. This is possible by leveraging advanced analytics for customer data to figure out non-compliant travels and thus cut the processing time and effort drastically. By using advanced analytics companies are able to focus on the small minority of travelers that cause the most risks. As a result of this, the companies are able to cut out more than half the time and effort, while increasing the overall compliance.
Artificial intelligence offers many advantages to the business travel segment, however, its biggest is the real-time data which it imparts to the travelers. With deep learning technology can resolve many problems faced by the travel managers on a regular basis. One of the top issues faced by travel managers is to quantify savings. Travel savings are difficult to quantify mainly because of the data quality. Depending on how the employees book their trips and report expenses, a travel manager might have access to incomplete data. With process the data and with deep learning, we can eliminate the dark points and then present the new data that emerges for better analyzes and presentations of the results.
Once risks are correctly identified, it is time to analyze them and prioritize that which will have the greatest impact on your project. Assessing the wrong list, or an incomplete data of risks, will do a damage on your business, so it is critical that you do the right moves.
AI can reverse the cycle of low profitability through intelligent automation and innovation diffusion. To capitalize on these benefits, manufacturing companies need a partner that can simplify and streamline the AI integration process.