Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
Amazon's 2019 Climate Pledge calls for a commitment to net zero carbon across their businesses by 2040. Since then, the company has reduced the weight of their outbound packaging by 33%, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.5 billion shipping boxes. With less packaging used throughout the supply chain, volume per shipment is reduced and transportation becomes more efficient. The cumulative impact across Amazon's enormous network is a dramatic reduction in carbon emissions. To make this happen, the customer packaging experience team partnered with AWS to build a machine learning solution powered by Amazon SageMaker.
With both government and companies eagerly adopting artificial intelligence (AI) strategies, we explore how AI could also streamline and scale your business. We examine the potential opportunities and risks that come with using AI, and what the future of AI and business looks like. The CSIRO defines AI as "a collection of interrelated technologies used to solve problems autonomously and perform tasks to achieve defined objectives, in some cases without explicit guidance from a human being." Subfields of AI include machine learning, computer vision, human language technologies, robotics, knowledge representation and other scientific fields. For instance, AI is already being used in autonomous emergency breaking (helping reduce 1,137 vehicle-related deaths per year) and in maintaining Sydney Harbour Bridge (using machine-learning and predictive analytics to identify priority locations for maintenance).
Malaysian operator Maxis has announced it is working with Google Cloud to integrate data analytics into its business, from consumers to enterprise, network, retail channels and employees. The company's digital analytics transformation programme entails transitioning 100 percent of its business intelligence, data analytics and machine learning on-premise workloads to the cloud. Maxis has also established its Big Data and Advanced Analytics and AI Center of Excellence with data scientists and commitment programmes. Maxis is leveraging Artificial Intelligence and Machine Learning (AI/ML) services from Google Cloud, as well as from Google Cloud partners' technology solutions. Google Cloud and Maxis also plan to jointly develop a curated career development programme to build technical knowledge and in-house expertise, and grow the number of Google Certified Data Engineers within the organisation.
Data science is a field focused on extracting knowledge from data. Put into lay terms, obtaining detailed information applying scientific concepts to large sets of data used to inform high-level decision-making. Take the ongoing COVID-19 global pandemic for example: Government officials are analyzing data sets retrieved from a variety of sources, like contact tracing, infection, mortality rates, and location-based data to determine which areas are impacted and how to best adjust on-going support models to provide help where it is most needed while trying to curb infection rates. Big data, as it is often called, is the collective aggregation of large sets of data culled from multiple digital sources. These swaths of data tend to be rather large in size, variety (types of data), and velocity (the rate at which data is collected).
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Data science is the most promising field in near future, with the advancement of technology and statistical models in recent times, a new data wave is knocking at our doors for a complete revolution. It relates to an interdisciplinary field of study that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. As diverse does this field sounds, its team also has to be diverse enough to carry out tasks efficiently! To understand this in a better way let's follow the pipeline for a data science project. The most important aspect of this job is to Understand the Business Problem at the beginning, in the meeting with clients, a data science professional asks relevant questions, understands and defines objectives for the problem that needs to be tackled.
Although we are still in the infancy of the AI revolution, there's not much artificial intelligence can't do. From business dilemmas to societal issues, it is being asked to solve thorny problems that lack traditional solutions. Possessing this endless promise, are there any limits to what AI can do? Yes, artificial intelligence and machine learning (ML) do have some distinct limitations. Any organization looking to implement AI needs to understand where these boundaries are drawn so they don't get themselves into trouble thinking artificial intelligence is something it's not.
Is there anything that can stop AI? As the novel Covid-19 pandemic forces the world to put on its brakes, AI technologies like machine learning – AutoML in particular – have been continuing to develop at break-neck speeds at the beginning of the new decade. Following a recent breakthrough by Google scientists at the start of a period of enforced lockdown, AutoML is seeing a wave of new progress in correlation with the explosion of big data, advanced analytics and predictive models. The increasing amount of viable data has meant that AI, machine learning (ML) and data science is undergoing reams of data and training that has served to boost the technology exponentially. AutoML in 2020, can perform data pre-processing, as well as Extraction, Transformation and Loading tasks (ETL).
The gap between leaders versus laggards in AI has widened significantly in the last 6 months, even as leaders are investing big time on pilot projects to transform business teams with AI and Deep Learning. In a powerful survey finding, market research firm ESI ThoughtLab has found out APAC region leads (14.1 Billion USD) in average revenue earned through the adoption of AI applications in 2020. North America ($13.9 billion) and EU ($12.7 Billion) have also reported significant revenue growth from AI adoption. Laggards in AI can drive home success with AI investments by developing a culture of learning and sharing knowledge. ESI ThoughtLab reports AI leaders are constantly amplifying their data science talent pool by acquiring AI businesses.
We are living in a time where everything is digital. Disruptive technologies like artificial intelligence (AI) has become central to this transformation. From retail to Fintech and cybersecurity to predictive analytics, tech pundits avow that AI now plays an essential cog in the future of these industries and disciplines. However, through some alarmists argue that AI is stealing jobs through automation and robotics, on the contrary, it has been observed that AI is also adding new job roles every day to the existing employment pool. Researchers have tracked down new job roles, occupations and emerging industries, in the AI landscape that can help us understand the job market better.