Bard Underwhelms in Debut

Still lots of Work to do for Human level performance,  An Update for chatbots, expensive reaction.

Google's Bard AI bot mistake wipes $100bn off shares   in the  BBC

Google unveiled its new bot called Bard,       By Natalie Sherman

Google is searching for ways to reassure people that it is still out in front in the race for the best artificial intelligence technology.

And so far, the internet giant seems to be coming up with the wrong answer.

An advert designed to show off its new AI bot, showed it answering a query incorrectly.

Shares in parent company Alphabet sank more than 7% on Wednesday, knocking $100bn (£82bn) off the firm's market value.

In the promotion for the bot, known as Bard, which was released on Twitter on Monday, the bot was asked about what to tell a nine-year-old about discoveries from the James Webb Space Telescope.

It offered the response that the telescope was the first to take pictures of a planet outside the earth's solar system, when in fact that milestone was claimed by the European Very Large Telescope in 2004 - a mistake quickly noted by astronomers on Twitter.

"Why didn't you factcheck this example before sharing it?" Chris Harrison, a fellow at Newcastle University, replied to the tweet.

Investors were also underwhelmed by a presentation the company gave about its plans to deploy artificial intelligence in its products.

Google has been under pressure since late last year, when Microsoft-backed OpenAI unveiled new ChatGPT software. It quickly became a viral hit for its facility in passing business school exams, composing song lyrics and answering other questions.

Microsoft this week said a new version of its Bing search engine, which has lagged Google for years, would use the ChatGPT technology in an even more advanced form.

Though investors have embraced the push for artificial intelligence, sceptics have warned rushing out the technology raises risks of errors or otherwise skewed results, as well as issues of plagiarism.

A Google spokesperson said the error highlighted "the importance of a rigorous testing process, something that we're kicking off this week with our Trusted Tester programme".  ... ' 

Learning from Big Mistakes

How about linking it to after action reviews?   Move closer to the process and the results.

How to Learn from the Big Mistake You Almost Make  by Kristen Senz

A brush with disaster can lead to important innovations, but only if employees have the psychological safety to reflect on these close calls, says research by Amy C. Edmondson, Olivia Jung, and colleagues.

What if businesses could learn from their worst mistakes without actually making them? How might the same progress and innovation occur, without firms incurring the costs associated with such errors?

The results of a recent study about close calls in health care suggest that when people feel secure about speaking up at work, incidents in which catastrophe is narrowly averted rise to the surface, spurring important growth and systems improvement.

“People don't pay enough attention, especially in the business world, to the potential goldmine of near-misses,” says Harvard Business School Professor Amy C. Edmondson, who studies psychological safety and organizational learning.

Incidents that almost result in loss or harm often pass unnoticed, in part because workers worry about being associated with vulnerability or failure. But when leaders frame near misses as free learning opportunities and express the value of resilience to their teams, the likelihood that workers will report such incidents increases.

That was the main finding of Resilience vs. Vulnerability: Psychological Safety and Reporting of Near Misses with Varying Proximity to Harm in Radiation Oncology, a study by Edmondson, the Novartis Professor of Leadership and Management at Harvard Business School, and Olivia Jung, a doctoral student at HBS. Co-authors on the paper, which was published in The Joint Commission Journal on Quality and Patient Safety, included UCLA physicians Palak Kundu, John Hegde, Michael Steinberg, and Ann Raldow, and medical physicist Nzhde Agazaryan.  ... "

Alexa Self Learning to Correct Mistakes

In all conversation there is adjustments of our interactions.Good piece here that shows how this is being proposed for a common assistant.

Amazon Uses Self-Learning to Teach Alexa to Correct its Own Mistakes

The digital assistant incorporates a reformulation engine that can learn to correct responses in real time based on customer interactions .    By Jesus Rodriguez in Towards Data Science

 Digital assistant such as Alexa, Siri, Cortana or the Google Assistant are some of the best examples of mainstream adoption of artificial intelligence(AI) technologies. These assistants are getting more prevalent and tackling new domain-specific tasks which makes the maintenance of their underlying AI particularly challenging. The traditional approach to build digital assistant has been based on natural language understanding(NLU) and automatic speech recognition(ASR) methods which relied on annotated datasets. Recently, the Amazon Alexa team published a paper proposing a self-learning method to allow Alexa correct mistakes while interacting with users.

The rapid evolution of language and speech AI methods have made the promise of digital assistants a reality. These AI methods have become a common component of any deep learning framework allowing any developer to build fairly sophisticated conversational agents. However, the challenges are very different when operating at the scale of a digital assistant like Alexa. Typically, the accuracy of the machine learning models in these conversational agents is improved by manually transcribing ... '

Learning from Mistakes

Ultimately most important.  How do we learn from mistakes, and the contexts that created those mistakes?  A form of causal reasoning that we found most useful in our efforts.    A form of maintenance to0 that could be fed back into a loop for updating data based training.

Worker Robots that Learn from Mistakes
By University of Leeds (U.K.)

A robot arm attempts to clear a cluttered table.
University of Leeds scientists are using automated planning and reinforcement learning to train a robot to find an object in a cluttered space and move it.

Computer scientists at the University of Leeds in the U.K. are using the artificial intelligence techniques of automated planning and reinforcement learning to train a robot to find an object in a cluttered space and move it.

The goal is to develop robotic autonomy, a state in which the machine can assess the unique circumstances presented in a task and find a solution.

The main challenge is that in a confined area, a robotic arm may not be able to grasp an object from above, and instead must plan a sequence of moves to reach the target object in a way that allows it to grasp the object.

Said Leeds researcher Wissam Bejjani, "Our work is significant because it combines planning with reinforcement learning. A lot of research to try and develop this technology focuses on just one of those approaches."..... '

From University of Leeds (U.K.)