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Attack of the Robots: Artificial Intelligence, Automation, and Job Search

and, has held the positions of veteran’s counselor, career assessment counselor, and employment trainer.

Jim has trained over 7,000 people in career exploration and job search techniques during his tenure.

Jennifer Weggeman specializes in coaching job seekers, career changers and freelancers using creative problem-solving techniques.

For over 25 years, she has worked with a large network of executive leaders in corporate, government, not for profit and entrepreneurial organizations.

Weggeman provided job search and career related support to international companies relocating employees &

It is a great opportunity to network with other job seekers and get advice and guidance from a team of dedicated career development professionals.

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.

RELATED ARTICLES20 examples of artificial intelligence shaking up business as usual How AI is changing the banking and finance industries The robots will see you now: How artificial intelligence is revolutionizing healthcare  

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.

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.

Why do tech companies tend to use AI and ML interchangeably?

October 15, 2018, by Roberto Iriondo — Last updated: August 23, 2019 Recently, a report was released regarding the misuse from companies claiming to use artificial intelligence [29] [30] on their products and services.

Last year, TechTalks, also stumbled upon such misuse by companies claiming to use machine learning and advanced artificial intelligence to gather and examine thousands of users’ data to enhance user experience in their products and services [2] [33].

Often the terms are being used as synonyms, in other cases, these are being used as discrete, parallel advancements, while others are taking advantage of the trend to create hype and excitement, as to increase sales and revenue [2] [31] [32] [45].

For instance, if you provide a machine learning model with a lot of songs that you enjoy, along their corresponding audio statistics (dance-ability, instrumentality, tempo or genre), it will be able to automate (depending of the supervised machine learning model used) and generate a recommender system [43] as to suggest you with music in the future that (with a high percentage of probability rate) you’ll enjoy, similarly as to what Netflix, Spotify, and other companies do [20] [21] [22].

In a simple example, if you load a machine learning program with a considerable large data-set of x-ray pictures along with their description (symptoms, items to consider, etc.), it will have the capacity to assist (or perhaps automatize) the data analysis of x-ray pictures later on.

The type of machine learning from our previous example is called “supervised learning,” where supervised learning algorithms try to model relationship and dependencies between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships, which it has learned from previous data-sets [15] fed.

According to Andrew Moore [6] [36] [47], Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.” That is a great way to define AI in a single sentence;

Prior works of AI utilized different techniques, for instance, Deep Blue, the AI that defeated the world’s chess champion in 1997, used a method called tree search algorithms [8] to evaluate millions of moves at every turn [2] [37] [52] [53].

Fields such as speech and face recognition, image classification and natural language processing, which were at early stages, suddenly took great leaps [2] [24] [49], and on March 2019–three the most recognized deep learning pioneers won a Turing award thanks to their contributions and breakthroughs that have made deep neural networks a critical component to nowadays computing [42].

For those who had been used to the limits of old-fashioned software, the effects of deep learning almost seemed like “magic” [16], especially since a fraction of the fields that neural networks and deep learning are entering were considered off-limits for computers.

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