IBM's California research lab sits atop a green hill in Almaden, 15 miles south of downtown San Jose. There aren't any signs that suggest if you drive up the narrow road that wraps around the hill you'll find a research facility at the top. No signs that the research center is home to a Fortune 500 company. No signs -- even inside -- that the company once dominated the personal computer industry. After decades in the spotlight as a hardware-centric firm selling PCs, servers and mainframes, the 105-year-old tech giant has made a dramatic shift into a realm that few understand: cognitive computing.

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The Chinese web search giant Baidu is looking to expand its autonomous car program to the United States by bringing a 100-person team to Silicon Valley.

Another self-driving car startup is about to hit the roads. The Wall Street Journal reports that Drive.ai, a Silicon Valley startup that received 12 million in funding last year, has been granted a license to test autonomous vehicles on California roads. It's the 13th company to receive permission. Rather than programming a car so that it knows exactly what to do, Drive.ai That means, like a human, the more time it spends driving and the more scenarios it encounters, the better it'll get at responding to them.

By 2020, we will all have an Invisible Friend. Whether we call it Siri, Alexa, OK Google, or a chatbot, we are entering a world where an intelligence assistant recognizes our "intent." This could spawn a massive consumer behavior shift, as AI-influenced bots would mean far fewer Google searches by humans. This invisible friend would learn from its mistakes, maintain context, and continue to expand into new areas of expertise through judicious use of Knowledge Management (see below landscape). Although 2020 is our destination, now is a time of heightened activity among the companies that provide the elements of Intelligent Assistance.

According to Weisensee et al., Data warehouse architecture follows following principles: ETL process is the foundation of BI. Success and failure of BI projects depends upon ETL process. It plays a vital role to integrate and enhance the worth of data. After the extraction, cleansing and arrangement of data, it will be loaded into data warehouse. In short, ETL is the transferring process of data from data source to the target data warehouse.

Justin Sullivan/GettyEatsa is San Francisco's fully automated fast food restaurant where orders appear in a cubby. At San Francisco's first fully automated restaurant, meals appear in little glass cubbies, just 90 seconds after customers order and pay on wall-mounted iPads. It's a human-less experience – no waitstaff, no cashier, no one to get your order wrong and no one to tip. The moment before the meal appears, the see-through display screen that fronts the cubbies goes black for the few seconds when you might catch sight of the hand that feeds you. Eatsa has not yet achieved total automation.

Two announcements yesterday (April 21) suggest that deep learning algorithms rival human skills in detecting cancer from ultrasound images and in identifying cancer in pathology reports. Samsung Medison, a global medical equipment company and an affiliate of Samsung Electronics, has just updated its RS80A ultrasound imaging system with a deep learning algorithm for breast-lesion analysis. The "S-Detect for Breast" feature uses big data collected from breast-exam cases and recommends whether the selected lesion is benign or malignant. It's used in in lesion segmentation, characteristic analysis, and assessment processes, providing "more accurate results." "We saw a high level of conformity from analyzing and detecting lesion in various cases by using the S-Detect," said professor Han Boo Kyung, a radiologist at Samsung Medical Center.

Researchers from the Massachusetts Institute of Technology have created an AI system that can predict a cyberattack before it happens in 85% of incidents. Analyst-driven systems rely on rules created by people and consequently can't detect attacks that don't adhere to those rules, whereas machine-learning systems rely on anomaly detection, which tends to generate false positives that have to be investigated by people.MIT researchers have announced that they've concocted a new artificial intelligence system capable of successfully detecting 85% of cyber-attacks. Part of the challenge of merging human- and computer-based threat detection has been the manual labeling of data for algorithms.The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. It then reports this activity to a human analyst who can then judge if there's an actual attack.With that feedback, it takes on board whether or not it should be classifying the events as attacks or not, then refines its internal models.According to Engadget, Kaylan Veermachaneni, co-creator of the system, said that one should think of the new system as a virtual analyst. In the near future the industry and federal regulators will need to figure out a balance between the need of cyber security and protecting consumers' privacy. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway.And the more data it analyses, the more accurate it becomes.

This year has been a good one for robots in the epic battle of Man vs. Machine. It's been decades since the first computer beat a chess champion, but the ancient Chinese game of Go -- which supposedly has more possible moves than there are atoms in the universe -- had always escaped the robot's grasp. At least until Google's AlphaGo took four out of five games against the reigning human world champion. Well, basically it taught itself. Google's DeepMind artificial intelligence subsidiary spent the last two years developing this database of 100,000 human-played rounds of Go which it fed into AlphaGo which then played against itself millions of times, using machine learning and neural networks to improve until it was finally the victor. But then when you take that machine learning and artificial intelligence to the next level of deep learning, well, your neurons take a hit.