It should be noted that fearing Siri, Alexa, Cortana, or your smart device autocorrecting you as dangerous AI may be grossly exaggerated, because they are not General Artificial Intelligence (GAI) and they are merely created as programs to perform limited and specific tasks. However, while we do not have the sort of GAI yet capable of experiencing human-like consciousness and thank God we arent anywhere close to it, the AI and cognitive Bots rising technologies today can still be used for ill and pose a threat to our world.

Some of the threat is attributed not to the superintelligence of AI and cognitive Bots but rather to the fact that they still rely on humans to provide the data and instructions to train them to do specific task very well but they still dont understand it the same way humans do. For example, Google mail has a new Smart Reply feature that suggests replies to emails. The Smart Reply feature identified Sent from my iPhone as a common response. It also wanted to suggest I love you as a response to many different types of emails, including work emails. Another example that shows that AI Agents and cognitive Bots dont understand what or why they learn and often end up learning how to solve a different problem from what you wanted, is in Gaming.

The following is list of fun examples where AI Agents created to play games and assigned goals just learned to game the system:

• Creatures bred for speed grow really tall and generate high velocities by falling over.
• Agent kills itself at the end of level 1 to avoid losing in level 2.
• Agent pauses the game indefinitely to avoid losing.
• In an artificial life simulation where survival required energy but giving birth had no energy cost, one species evolved a sedentary lifestyle that consisted mostly of mating in order to produce new children which could be eaten (or used as mates to produce more edible children).
• Since the AIs were more likely to get killed if they lost a game, being able to crash the game was an advantage for the genetic selection process. Therefore, several AIs developed ways to crash the game.
• Neural nets evolved to classify edible and poisonous mushrooms took advantage of the data being presented in alternating order and didnt actually learn any features of the input images.

According to this list, some of the solutions the Ai Agents chose may sound clever, but none of them understood what they were doing. In all cases, Ai Agents were assigned a goal and they learned a way to accomplish it. So, if the goal is for example avoid losing in a computer game, the Ai Agent would press the pause button because that would be the fastest solution not to lose the game. From these examples, researchers concluded that this type of Ai Agents and cognitive Bots actions would be a source of risk to humans if the field was not about winning games but rather treating a human body for example.

According to the Malicious Use of Artificial Intelligence report, compiled by experts from a number of institutions including the University of Cambridge and research firm OpenAI, advancement in AI and cognitive Bots could be exploited by rogue states, terrorists and criminals.

The report outlines three areas physical, digital and political where AI is most likely to be exploited, and describes scenarios of how AI attacks might play out. The following are the 3 areas of potential threat:

1. Remote-controlled car crashes
2. Sophisticated phishing
3. Manipulating public opinion

Research these 3 areas of potential threat and compile your own report of where they are most likely to inflict most of the harm on us physically, digitally and politically citing some example, i.e. use of Deep Fake to influence or derail elections etcCan you think of any ways or strategy to mitigating the risks.

The Future Of A.I. Forecasted cumulative global artificial intelligence revenue 2016-2025, by use case (U.S. dollars) $8,097.9m Static image recognition, classification, and tagging Algorithmic trading strategy performance improvement Efficient, scalable processing of patient data $7,540.5m $7,366.4m $4,680.3m Predictive maintenance Object identification, detection, classification, tracking* Text query of images $4,201.0m $3,714.1m Automated geophysical feature detection $3,655.5m $3,566.6m Content distribution on social media Object detection and classification - avoidance, navigation Prevention against cybersecurity threats $3,169.8m $2,472.6m C O O * From geospatial images @StatistaCharts Source: Tractica statista The Future Of A.I. Forecasted cumulative global artificial intelligence revenue 2016-2025, by use case (U.S. dollars) $8,097.9m Static image recognition, classification, and tagging Algorithmic trading strategy performance improvement Efficient, scalable processing of patient data $7,540.5m $7,366.4m $4,680.3m Predictive maintenance Object identification, detection, classification, tracking* Text query of images $4,201.0m $3,714.1m Automated geophysical feature detection $3,655.5m $3,566.6m Content distribution on social media Object detection and classification - avoidance, navigation Prevention against cybersecurity threats $3,169.8m $2,472.6m C O O * From geospatial images @StatistaCharts Source: Tractica statista