If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Natural Language Trends in Visual Analysis - Aug 6 In this latest Data Science Central webinar, Vidya will discuss how natural language can be leveraged in various aspects of the analytical workflow ranging from smarter data transformations, visual encodings, autocompletion to supporting analytical intent. More recently, chatbot systems have garnered interest as conversational interfaces for a variety of tasks. Machine learning approaches have proven to be promising for approximating the heuristics and conversational cues for continuous learning in a chatbot interface.
This is just meant as a friendly introduction to a topic that every computer science and data science program I know off explores in an entire course or a few. Working with any kind of algorithm starts with learning a set of data structures associated with it. This makes sense since most algorithms work on some kind of data that must be stored and held somehow, somewhere. That's where data structures come handy! Data Structures are used to organize information and data in a variety of ways such that an algorithm can be applied to the structure in the most efficient way possible.
In the future, we won't be discovering new drugs or cures to cancer -- they will be designed by computers. And that process of bioengineering has only become more urgent as a result of the coronavirus pandemic and the need to find a solution fast. Vijay Pande, a partner at Andreessen Horowitz's biotech fund, has been investing at the intersection of health care, biology and computer science for the last five years. Coronavirus has amplified the need for this kind of technology, but the pandemic also played a more important role: shifting the mindset of patients and doctors alike. People have become more attuned to what they need to do to stay healthy, while doctors are more open trying to new technologies and techniques, Pande told Protocol.
Researchers at Valuates Reports say the global use of artificial intelligence (AI) in retail markets is set to reach a market share of $14.7 Billion by 2026 from $2.7 Billion in 2020, at a CAGR of 32.7%. The versatility of the technology makes it valuable for optimizing the supply chain, using existing data to improve conversion, and customizing shopping experiences through predictive modeling and micro-targeting. The company's report, titled "Global Artificial Intelligence(AI) in Retail Market Size, Status and Forecast 2020-2026", points out several trends and factors influencing the use of AI in retail markets. Some major companies in the AI arena include IBM, Microsoft, Nvidia, Amazon Web Services, Oracle, SAP, Intel, Google, Sentient Technologies, Salesforce, and Visenze. For more details, ask for a sample of the "Global Artificial Intelligence(AI) in Retail Market Size, Status and Forecast 2020-2026" report.
A team of USC researchers has created a hate speech classifier that is more context-sensitive, and less likely to mistake a post containing a group identifier as hate speech. Understanding what makes something harmful or offensive can be hard enough for humans, never mind artificial intelligence systems. So, perhaps it's no surprise that social media hate speech detection algorithms, designed to stop the spread of hateful speech, can actually amplify racial bias by blocking inoffensive tweets by black people or other minority group members. In fact, one previous study showed that AI models were 1.5 times more likely to flag tweets written by African Americans as "offensive"--in other words, a false positive--compared to other tweets. Because the current automatic detection models miss out on something vital: context.
Spartan Controls Ltd. and AltaML forge a formal partnership and strategic alliance to build applications for the process industries in Western Canada. They are combining their respective strengths in automation and applied artificial intelligence/machine learning (AI/ML) to modernize process industries in the region. Modern industrial processes are employing an increasing number of industrial sensors, complex process control systems, and a plethora of other industrial data systems in the safe and efficient operation of these facilities. These processes generate large volumes of data which creates the opportunity to leverage modern AI/ML technologies in a wide variety of applications. According to the partners, conceptualizing, developing, and commercializing AI/ML solutions will generate significant business value for process industries by leveraging AltaML's AI/ML development capability with Spartan's domain knowledge and expertise around industrial processes, automation, optimization, equipment, and process reliability.
According to several studies by Intel spanning 2018, 2019, and 2020, AI and edge computing make it possible to positively identify up to 99% of visible manufacturing defects before a product ever leaves the line. "One of the most important things manufacturers care about is product quality," says Brian McCarson, Vice President and Senior Principal Engineer, Internet of Things Group (IOTG) at Intel Corporation and a featured speaker at Transform, VentureBeat's upcoming digital conference. "Manufactures prefer throwing away fewer defective products. They strive to have less rework and fewer customer returns. They also want to reduce the cost of their operations by making their tools and processes more efficient, and improve the reliability of their machines so they can proactively do maintenance before it is too late and have more predictable uptime."
We talk a lot about marketing challenges and, oftentimes, those challenges relate to marketers of certain maturities, industries, strategies, etc. But what is one universal challenge for all marketers? With our Predictive Audiences capability coming soon, Marketo Engage is transforming this fundamental piece of marketing in the most powerful way – through artificial intelligence. Predictive Audiences will be included in our Prime and Ultimate packages and available as an add-on for our Select package. Every marketer faces a similar dilemma with each program: reaching the most engaged and relevant audience and maximizing conversion without fatiguing the recipients and driving opt-outs.
GumGum, a contextual ad exchange, has partnered with Simplaex's Rivr, an AI-powered traffic shaping tool to improve its Supply Side Platform's (SSP) programmatic auction performance. Rivr aided GumGum in responding to recent global events that have led to a surge in supply and steadily declining demand by adjusting ad operations to increase efficiency and performance. The Coronavirus continues to threaten economic stability. As a result, spending on advertising has been slashed, while time spent online has skyrocketed, leaving the supply side with a surge in available inventory. Rivr quickly adjusts the models to respond to this glut, assisting GumGum in mitigating the gap for its SSP and DSP partners.
Cartoonization is itself a classic art but, the evolution in the field of Machine Learning is covering almost every realm. Today, in this article we are going to learn about a new method called ' White box Cartoonization' that reconstructs a photo into an animation style focusing on the expression extracting elements, making the plans entirely controllable and flexible. I bet everyone must be fond of cartoons and they must have been an integral part of your childhood too. And apart from these relishing memories, it might be a career choice for some of us. But Machine Learning is constantly evolving thus expanding in almost every field.