BS Summary: This article contains 10 faulty reasoning types, including Indoctrination, Confirmation Bias, and Optimism Bias, with Attempt to Sell a Product or Service as the most egregious example at 12.1% saturation with 89 hits. Analysis detected 368 faulty-reasoning hits from 735 analyzed words, generating a BS Score of 19.1% and a BS Rank of 9% (26,359 of 28,846 articles). This article is better (less manipulative) than 91.40% of the article peer group.

Lasers, self-driving cars and chess-playing robots were on prominent display at Mountain View’s 11th annual Technology Showcase on July 30, which gave residents a glimpse of the innovations being worked on throughout the city and exhibited resources they can use for their own purposes. 
More than 500 visitors flitted in and out of the free showcase at Civic Center Plaza, co-hosted by the city and Mountain View Chamber of Commerce. 
Visitors could peruse 37 exhibits featuring dozens of interactive and demonstrative technologies, followed by a two-hour panel on artificial intelligence’s uses for small businesses in the evening. 
Hundreds of visitors attended the tech showcase in Mountain View. 
Photo by Michael Molcsan. 
There was a broad variety between the exhibits. 
NASA’s Ames Research Center brought stacks of a futuristic block known as “voxels” and the machines that assemble them into structures in outer space. 
A Waymo was parked just across the street, showcasing the autonomous driving technology . 
In the plaza, visitors were challenged to a game of chess against smAiT’s social robots, and invited to engrave messages on metal with XTOOL’s laser cutters. 
To Chamber of Commerce President and CEO Peter Katz, the variety shows how much is going on across the city, from established companies to inventors working in their garages. 
“We are still one of the centers of innovation,” Katz said. 
“We are still an attractive hub to bring it in and export it to the world. 
We’re a small city that changes the world.” 
An attendee asks the RB Labs humanoid robot a question at the tech showcase. 
Photo by Michael Molcsan. 
Grant Xu, a rising sophomore at Prospect High School, attended the showcase with Verilog-Meetup , an educational group focused on teaching teenagers and children the basic principles of circuit boards. 
While he was there to present his own work on field programmable gate arrays, or FPGAs, Xu said he was interested in seeing the other exhibits. 
“You get to present the cool stuff you made, and you get to see all the other cool stuff other people made,” Xu said. 
As a hobby chess player, Xu was especially interested in smAiT’s chess-playing robot. 
While he lost his match against it, he said he wasn’t surprised, and appreciated the amount of effort and innovation in its creation. 
Steven Schaadd of XTOOL demonstrates a laser engraver. 
Photo by Michael Molcsan. 
These interactions are why smAiT founder and CEO Fan Tan Smith was excited to bring the robots out to this event. 
She launched smAiT two years ago to manage the full lifecycle of robots as a workforce, everything from the manufacturing to the programming and fleet management. 
Along with its social robots, Smith said smAiT offers a hospitality robot and a security robot, performing tasks associated with those industries, such as reception work or patroling. 
Smith said the robots were launched about six months ago in businesses around Silicon Valley, including some in Mountain View, and they’ve seen a big demand as businesses face a labor shortage and rising costs. 
At events like this, she’s glad to see visitors interact with the robots, she said. 
“It’s good to interact with people,” Smith said. 
“In the end, it’s to serve the people, so this is a great opportunity.” 
NASA intern Mario Jerez stands next to the Autonomous Reconfigurable Mission Adaptive Digital Assembly System (ARMADAS) project during the tech showcase. 
Photo by Michael Molcsan. 
The showcase’s panel featured representatives from LinkedIn and Intuit, along with Ryan Keenan, director at the educational platform DeepLearning.AI , and Danny Barry, store manager of St. 
Stephens Green, located on Castro Street. 
The panelists discussed how people are interacting with AI, especially when it comes to running a business. 
Barry described an example of how he used AI to build a reservation tracker for his restaurant, catered to its specific needs. 
But they agreed users need to approach AI thoughtfully as a tool, not a replacement. 
“AI can absolutely be there to help you, but if you lose sight of your individual point of view, your ideas, your core content, that’s probably when you’ve taken it too far,” Judy Nam, vice president of small business marketing for LinkedIn, said. 
This story originally appeared in the Mountain View Voice . 
B. 
Sakura Cannestra is a reporter for Embarcadero Media. 
The post Mountain View tech showcase amazes crowds appeared first on San José Spotlight . 
Article reasoning-pattern comparisonThis article: 5.9%B. Sakura Cannestra: 1.8%Mountain View Voice: 2.0%Confirmation Bias5.9%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Anchoring Bias0.0%This article: 0.0%B. Sakura Cannestra: 1.2%Mountain View Voice: 2.2%Availability Heuristic0.0%This article: 3.9%B. Sakura Cannestra: 1.0%Mountain View Voice: 0.4%Representativeness Heuristic3.9%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Hindsight Bias0.0%This article: 0.0%B. Sakura Cannestra: 0.3%Mountain View Voice: 0.7%Overconfidence Bias0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 2.4%Framing Effect0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.5%Loss Aversion0.0%This article: 1.5%B. Sakura Cannestra: 0.4%Mountain View Voice: 0.5%Status Quo Bias1.5%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.2%Sunk Cost Effect0.0%This article: 5.9%B. Sakura Cannestra: 4.7%Mountain View Voice: 2.5%Optimism Bias5.9%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 1.4%Pessimism Bias0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 3.2%Negativity Bias0.0%This article: 3.1%B. Sakura Cannestra: 2.9%Mountain View Voice: 1.1%Self-Serving Bias3.1%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.5%Fundamental Attribution Error0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.0%Actor-Observer Bias0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 1.4%In-Group Bias0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.5%Halo Effect0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Horn Effect0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.6%Recency Bias0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Primacy Effect0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.0%Blind-Spot Bias0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.2%Ad Hominem0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.0%Straw Man0.0%This article: 0.0%B. Sakura Cannestra: 1.5%Mountain View Voice: 1.5%Appeal to Authority0.0%This article: 2.0%B. Sakura Cannestra: 0.5%Mountain View Voice: 0.8%False Dilemma2.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.6%Slippery Slope0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Circular Reasoning0.0%This article: 0.0%B. Sakura Cannestra: 2.4%Mountain View Voice: 2.2%Hasty Generalization0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.3%Red Herring0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.7%Bandwagon0.0%This article: 0.0%B. Sakura Cannestra: 0.5%Mountain View Voice: 2.4%Appeal to Emotion0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.6%Begging the Question0.0%This article: 4.8%B. Sakura Cannestra: 1.2%Mountain View Voice: 1.4%Post Hoc (False Cause)4.8%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.0%Tu Quoque0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.6%Burden of Proof0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Appeal to Nature0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Composition/Division0.0%This article: 3.0%B. Sakura Cannestra: 0.7%Mountain View Voice: 1.9%Anecdotal3.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%No True Scotsman0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.7%Ambiguity (Equivocation)0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.0%Gambler’s Fallacy0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.2%Middle Ground0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.0%Personal Incredulity0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.2%Special Pleading0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Genetic Fallacy0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.8%Unattributed Quote0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.5%Quote-first Misdirection0.0%This article: 0.0%B. Sakura Cannestra: 0.2%Mountain View Voice: 0.8%Biased Writer Voice0.0%This article: 7.9%B. Sakura Cannestra: 2.0%Mountain View Voice: 1.0%Indoctrination7.9%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.5%Politically Left Leaning Bias0.0%This article: 0.0%B. Sakura Cannestra: 0.0%Mountain View Voice: 0.1%Politically Right Leaning Bias0.0%This article: 12.1%B. Sakura Cannestra: 4.9%Mountain View Voice: 0.5%Attempt to Sell a Product or S…12.1%

735 words analyzed.

Speakers

10speakers59%attributed speech304writer words
Selected voice

Judy Nam

100%flagged-word coverage
43 attributed words10.0% of attributed speech4.9% writer coverage
0%50.0%100.0%Indoctrination+95.1 ptsWriter: 4.9%Judy Nam: 100.0%100.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.