#SVMs are used in the field of bioinformatics to classify proteins and identify potential drug targets
#NeuralNetworks are used in the field of computer vision to recognize objects in images and videos
It is important to note that #SupervisedLearning only works well if the labeled data used for training is representative and accurate
#Overfitting occurs when a #SupervisedLearning model is too complex for the amount of training data, leading to poor performance on new data
#Underfitting occurs when a #SupervisedLearning model is too simple for the complexity of the problem, leading to poor performance on both training &new data
To prevent overfitting and underfitting, techniques such as cross-validation and regularization can be used
#FeatureEngineering is also a crucial step in the process of #SupervisedLearning, as selecting the right features can greatly improve the model's performance
It is important to keep in mind that #SupervisedLearning is not suitable for all problems, particularly unstructured and non-linear problems
In such cases, unsupervised learning or reinforcement learning may be more appropriate
By understanding the basics of #SupervisedLearning, one can make informed decisions about which approach is best for a given problem and improve their ability to solve real-world problems. #AI#DataScience
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🚨 THREAD: "The New AI PMF Playbook is Hype. Here’s Why You Should Think Twice." OpenAI’s Product Lead dropped a flashy new “AI-native PMF framework” claiming to replace everything we know about Product-Market Fit. Everyone’s applauding. But here’s what no one’s saying... 🧵👇
Ive will now wizard a H/W form factor "no one ever saw coming" to pair ChatGPTs questionable GPT strategy with an all-encompassing ultra "iPOD" moment for the merged entity. Their motivation? It's exolained in the next post by them!
These groups have been known to carry out cyber espionage, intellectual property theft, and sabotage. For instance, the #FancyBear APT group was responsible for the alleged 2016 US election interference
The IEEE GLOBAL GENERAL PRINCIPLES OF ‘ETHICALLY ALIGNED DESIGN’ initiative on the ethics of autonomous & intelligent systems (A/IS) includes 8 pillars
1. HUMAN RIGHTS: AI shall be created & operated to respect, promote, & protect internationally recognized human rights
A real-world example of this pillar:
1. A facial recognition system used by law enforcement that respects individuals’ privacy and does not discriminate against certain groups
2. WELL-BEING: AI creators shall adopt increased human well-being as a primary success criterion
A real-world example is a healthcare AI system that prioritizes patient outcomes and improves overall well-being, rather than just maximizing profits
Draw benefits from currently available AI tools to streamline your business, decrease costs, increase brand reach, create efficiencies, enhance your marketing mix & messaging, develop new ideas, & amaze & delight your customers
Murf enables anyone to convert text to speech, voice-overs, and dictations, and it is used by a wide range of professionals like product developers, podcasters, educators, and business leaders
Siloed development of AI by nation-states as National Security threat mitigation as well as the weaponizing of AI to infiltrate, and affect policy & population sentiment in adversary nations is a significant malignant threat to peace & exponentially increases the risk of conflict
AI algos harness volumes of macro & micro-data to influence decisions affecting people in a range of scenarios, from benign movie recommendations to less benign black-box creditworthiness tests, to malignant use by Alphabet Agencies for regime change
Artificial intelligence extends the reach of national security threats that can target individuals and whole societies with precision, speed, and scale