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11 Artificial Intelligence Interview Questions to Prepare

it’s predicted that AI and machine learning will impact all segments of our daily lives by 2025 with huge implications for industries ranging from transport and logistics to healthcare, home maintenance, and customer service.  With that dramatic increase in reliance on AI, massive investments are being made in both the technology and the skilled professionals needed to enable implementing and benefitting from the technology.

According to, the average salary for a professional with an AI certification is $110k a year in the U.S. Growing adoption, increased demand for certified professionals, and substantial salaries make a move into AI a wise choice for someone interested in this career field.

To position yourself for success as a job candidate who stands out from the crowd, you should be pursuing certifications in AI, as well as preparing ahead of time for crucial job AI interview questions.  Once you’ve lined up a job interview with a potential employer, you’ll have an opportunity to study that particular organization and its use of AI.

Possibilities include contract analysis, object detection, and classification for avoidance and navigation, image recognition, content distribution, predictive maintenance, data processing, automation of manual tasks, or data-driven reporting.  Intelligent agents are autonomous entities that use sensors to know what is going on, and then use actuators to perform their tasks or goals.

It refers to using multi-layered neural networks to process data in increasingly sophisticated ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data for continual improvement in the ability to recognize and process information.

CSPs are useful for AI because the regularity of their formulation offers commonality for analyzing and solving problems.  Supervised learning is a machine learning process in which outputs are fed back into a computer for the software to learn from, for more accurate results the next time.

32 Artificial Intelligence Companies Building a Smarter Tomorrow

From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence.

Meanwhile, revolutionary breakthroughs like self-driving cars may not be the norm, but are certainly within reach.  As the big guys scramble to infuse their products with artificial intelligence, other companies are hard at work developing their own intelligent technology and services.

By highlighting only the most relevant and interesting information, businesses can make quicker decisions regardless of the staff's experience with data or analytics.

Industry: Fintech Location: New York, New York What it does: AlphaSense is an AI-powered search engine designed to help investment firms, banks and Fortune 500 companies find important information within transcripts, filings, news and research. The technology uses artificial intelligence to expand keyword searches for relevant content.

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Using non-invasive blood tests, the company’s AI technology recognizes disease-associated patterns, providing earlier cancer detection and better treatment options.

Its LiDAR technology focuses on the most important information in a vehicle’s sightline such as people, other cars and animals, while putting less emphasis on things like the sky, buildings and surrounding vegetation.

By fusing problem solving, learning and memory technologies together, the company can build systems that learn and adapt without human assistance.

Called CARA A.I., the company's tech can search within the language, jurisdiction and citations of a user's uploaded documents and return relevant searches from the database.

Industry: Cloud, Robotics Location: Santa Clara, California What it does: CloudMinds provides cloud robot services for the finance, healthcare, manufacturing, power utilities, public sector and enterprise mobility industries.

Its cloud-based AI uses advanced algorithms, large-scale neural networks and training data to make smarter robots for image and object recognition, natural language processing, speech recognition and more.

The company's 'human-in-the-loop' platform uses human intelligence to train and test machine learning, and has powered AI projects for major companies like Oracle, Ebay SAP and Adobe.

From financial and insurance needs to travel and healthcare, the intelligent products perform duties and answer questions for tech support, billing, scheduling, purchases and policy information.

Industry: Big Data, Software Location: Mountain View, California What it does: Orbital Insight uses geospatial imagery and artificial intelligence to answer questions and gain insights invisible to the naked eye. Using data from satellites, drones, balloons and other aircrafts, the company can provide insights and forecasts to the agriculture and energy industries that normally wouldn’t be available.

Industry: Software Location: San Francisco, California What it does: OpenAI is a nonprofit research company with a mission to create safe artificial general intelligence (AGI). AGI aims to create machines with general purpose intelligence similar to human beings. With a focus on long-term research and transparency, OpenAI hopes to advance AGI safely and responsibly.

Sift uses thousands of data points from around the web to train in detecting fraud patterns. The technology helps payment processors, marketplaces, e-commerce stores and even social networks prevent fraud.

Industry: Software, Healthtech Location: Berkeley, California (US office) What it does: Zebra Medical Vision develops technology for radiology and medical imaging, enhancing the diagnostic abilities of radiologists while maximizing focus on patient care.

These algorithms will ultimately help medical professionals detect high-risk patients earlier and manage growing workloads with more accurate outcomes.

Spanning the agriculture, pharmaceutical and chemical industry, the company enables faster cultivation of microbes through automation software and a huge catalog of physical and digital DNA data.

Deep Learning Has Limits. But Its Commercial Impact Has Just Begun.

Studies have shown that AI can outperform human doctors at identifying breast cancer from ...

In December, deep learning pioneer Yoshua Bengio and AI researcher Gary Marcus engaged in a high-profile televised debate about whether deep learning was the right path forward for AI.

“They need much more data to learn a task than human examples of intelligence, and they still make stupid mistakes.” Without question, deep learning is an imperfect model of intelligence.

(For those looking for further reading on deep learning's shortcomings, Marcus' influential 2018 paper on the topic does an excellent job summarizing the issues.) There is an important distinction to draw here, however, which has not been clearly articulated in the recent public discourse.

Deep learning may be bumping up against conceptual limits as a model of intelligence, but opportunities to apply it to transform industries and enact massive real-world change still abound.

Put differently, if all basic AI research stopped today, and the only methodologies available to entrepreneurs were those already in existence, countless billions of dollars in enterprise value would still be created in the years ahead by applying deep learning to solve business problems in novel ways.

AI capabilities, as they exist today, are sufficiently advanced to enable transformative product innovation and value creation across industries: from agriculture to insurance, from healthcare to education, from transportation to construction, and beyond.

For entrepreneurs and operators willing to work through challenges related to product development, business model, consumer education and regulation, massive white space exists to bring AI-first products to market.

To give a few recent examples: in May 2019, a team of researchers from Google, Stanford and Northwestern published a study in which a deep learning model outperformed human physicians at detecting lung cancer from CT scans.

A few months later, a research team from NYU published a series of studies demonstrating AI's superior performance detecting breast cancer from mammograms.

In January 2020, a research group from Google and top medical research centers released another breast cancer detection study, with the AI system again outperforming humans.

In the January study, which received widespread media attention, the AI system produced a 9.4% reduction in false negatives and a 5.7% reduction in false positives relative to human radiologists.

There is a huge gap between publishing academic research and building a real company—with real patients, in real clinics, with real lives on the line—to commercialize that research.

In one example, a deep learning model trained to detect pneumonia performed at 93% accuracy when used on patients from the same hospital, but dropped as low as 73% when tested on patients from other locations.

“We will continue to explore and build upon our model, working with additional partners across the world, before considering bringing it into clinical practice,” said Shravya Shetty, a Google researcher who co-authored the paper.

Because vehicles in these sectors operate in highly controlled environments on repetitive routes at low speeds, the technological challenges are far simpler than for urban robotaxis.

Just as with the radiology example, while recent advances in AI mean that vast industry transformation has become technologically possible, this transformation has not yet come to full fruition due to the operational complexities of commercializing new technology.

Beyond Big Data: AI/Machine Learning Summit 2020

Registration Data Analytics, Artificial Intelligence, and Machine Learning are empowering businesses, solving tough challenges, and have the potential to make life easier and more productive for us all.  C-level executives in a variety of industries are using these technologies to make better business decisions and to serve their customers better and more efficiently.  However, navigating through the hype and the magnitude of data may be a challenge.  Now in its 5th iteration, Beyond Big Data: AI/ML Summit is a unique opportunity for managers on every level to learn more about the opportunities of these technologies while connecting with others in the industry.

With a focus on trends and best practices, the event aims to explore strategies, best practices and technologies surrounding data analysis, artificial intelligence and machine learning while keeping in mind the implications of regulations, privacy, data protection, and ethics that govern this field.  The one-day event will feature keynote speakers and several panel discussions.

We’ll look at the role that artificial intelligence plays in the autonomous space, but how it will help drivers stay alert and safe on the roads and keep the flow of traffic moving to get us from point A to point B more quickly.

Big data, artificial intelligence, and machine learning are increasingly being used as a tool to help businesses become more efficient, save money, and to meet the needs of both customers and staff in a better and more meaningful way.

Walk-in registrants, when accepted, must pay late/ onsite (additional fee required) with a credit card or check to obtain entry to the event, no exceptions. Refunds requested less than 2 business days prior to the event will not be granted.

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