Artificial intelligence includes machine learning (AI) and computers. The science that analyzes data and algorithms to mimic this field, data is analyzed, and algorithms are used to simulate the data. How humans learn, improving its accuracy over time. Machine learning has been an integral part of IBM’s history for a long time. Arthur Samuel is credited with coining the term “machine learning” through his research on the game of checkers. (PDF, 481 KB) (link resides outside IBM) that led to the term’s invention. When he played the checkers game on an IBM 7094 computer in 1962, Robert Nealey, the self-proclaimed checkers master, lost to the computer. This feat seems trivial compared to what is possible today. But it is considered a significant milestone in artificial intelligence due to its simplicity.
The technological advancements in storage and processing power have enabled. Some innovative products run on machine learning techniques. Machine learning is a game changer essential component of the growing field of data science. Statistical methods enable algorithms to be trained to make classifications or predictions. In turn, these insights then drive decision-making within applications and businesses in a way that impacts key growth metrics in the long run. Machine learning transforms data scientists as the market for big data continues to expand and grow. Their role will be to help identify the most relevant business questions and the data that can be used to answer those questions.
Neural Networks vs. Deep Learning:
Deep learning and machine learning are used and need to note. That have different characteristics worth noting. A range of aspects makes up artificial intelligence, including many subfields, including machine learning: deep learning, and neural networks. There is a subfield of machine learning called neural networks, whereas deep learning is a subfield of neural networks. The main difference between deep and machine learning lies in how each algorithm learns. It is also possible that “deep” machine learning can use labeled datasets to inform its algorithm, also known as supervised learning. Still, it is optional for it to do so. Doing this eliminates some of the human intervention required to process the data.
To operate the network, each node must be able to send data to the next layer if its output exceeds the specified threshold value. Without this, no data will pass on by that node to the next network layer. Deep learning refers to a network that consists of many layers, often called layers of a tree. Deep neural networks are neural networks with more than three layers, which include input and output. There is nothing special about a neural network with only three layers since this is all there is.
Using machine learning:
- There are three main parts to a machine learning algorithm’s learning system described by UC Berkeley (link resides outside IBM).
- Machine learning algorithms are generally used to make a prediction or classification using a decision process. Using input data that can be labeled or unlabelled, your algorithm would predict the pattern in the data based on the input data.
- There are two types of error functions in a model. An error function can assess a model’s accuracy if known examples can be compared to the model’s error function.
- A model optimization process reduces the discrepancy between the known example and model estimate if the model fits better to the training set. This process of “evaluate and optimize” will be repeated repeatedly by the algorithm, and weights will be updated autonomously until a threshold level of accuracy has been reached.
Methods of machine learning:
Three main types of machine learning models can be classified.
Machine learning under the supervision
As part of the supervised learning process, also known as supervised machine learning, labels are used to train algorithms to classify data or accurately predict outcomes. As a part of the cross-validation process, this ensures that the model does not overfit or underfit, as is the case with most models. Organizations can solve real-world problems at scale with supervised learning, like classifying spam. The methods used to learn run neural networks, naive Bayes, linear regression, logistic regression, random forests, and support vector machines.
Automated machine learning:
Known as unsupervised machine learning, unsupervised learning analyzes and clusters unlabeled datasets. As a result of these algorithms, hidden patterns or data groupings are discovered without the need for manual intervention on the user’s part. It is helpful for exploratory data analysis, cross-selling strategies, customer segmentation, image and pattern recognition, and cross-selling strategies. The dimensionality reduction process also uses Models with fewer features by reducing the number of elements within the model. Two of the most common approaches to this problem are principal component analysis (PCA) and singular value decomposition (SVD). Unsupervised learning also uses neural networks, k-means clustering, and probabilistic clustering.
In many ways, semi-supervised learning offers a happy medium between supervised and unsupervised learning. During the training phase, it uses a smaller set of labeled data to guide classifications and extract features from a more extensive, unlabeled data set. If you do not have enough labeled data for a supervised learning algorithm to be effective, then semi-supervised learning is a good alternative. Additionally, it would be helpful if the cost of labeling is too high to justify the effort.
Machine learning with reinforcement:
Unlike supervised learning, machine learning with reinforcement is similar to supervised learning, except that the algorithm isn’t trained using sample data. It is a model that learns by trial and error as it goes along.
IBM Watson® won Jeopardy! An example is the 2011 challenge. Reinforcement learning determined when to attempt an answer (or question), which square to select on the board, and how much to wager.
Methods used in machine learning:
Machine learning algorithms are widely used. Included are:
- The brain is formed by a network of neurons connected by many processing nodes, simulating how the brain works. Among other applications, neural networks recognize patterns, process images, recognize speech, and create images.
- Linear regression: This algorithm predicts numerical values based on a linear relationship between different values. Historical data could be used to predict house prices, for example.
- Logistic regression: Predicts yes/no answers to questions based on categorical response variables. In production lines, it’s used for quality control and spam classification.
- Clustering algorithms identify patterns in data using unsupervised learning to group them. Data scientists can use computers to identify differences between data items.
- Trees can use to predict numerical values (regression) and classify data. A decision tree is a branching sequence of linked decisions. Decision trees are easy to validate and audit, unlike neural networks, which are black boxes.
- Using a random forest, the machine learning algorithm combines the results from several decision trees to predict a value.
Machine learning in the real world:
The following are some examples of machine learning:
- Natural language processing (NLP) translates human speech into written form, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text. Mobile devices incorporate speech recognition to conduct voice search—e.g., Taking notes.
- Customer service: Online chatbots are replacing human agents along the customer journey, changing how we think about customer engagement. Users can ask chatbot questions about shipping, cross-sell products, or suggest sizes. Examples are virtual agents on e-commerce sites, messaging bots for Slack and Facebook Messenger, and virtual assistants and voice assistants.
- Computer vision: computers can derive meaningful information from digital images, videos, and other visual inputs. Among computer vision applications, photo tagging on social media, radiology imaging in healthcare, and self-driving cars.
- Artificial intelligence algorithms can help develop more effective cross-selling strategies using past consumption behavior data. Online retailers use this approach to make relevant product recommendations during checkout.
- AI-driven high-frequency trading platforms automate thousands to millions of daily trades without human intervention.
- Fraud detection: Financial institutions can spot suspicious transactions using machine learning. Models can be trained using known fraudulent transactions through supervised learning. An anomaly detection tool can identify transactions that seem out of the ordinary.
Machine learning challenges:
It has undoubtedly made our lives easier as machine learning technology develops. Several ethical issues have also been raised about AI technologies when implemented in businesses. Here are just a few of them:
Singularity of technology
Despite public interest in this topic, many researchers are not concerned about AI surpassing human intelligence soon. A technological singularity is also known as strong AI or superintelligence. In his definition of superintelligence, philosopher Nick Bostrom describes it as an intellect that outperforms the best human brain in virtually every field, including scientific creativity, general wisdom, and social skills. As we consider using autonomous systems, like self-driving cars, the idea of superintelligence raises some interesting questions. It’s unrealistic to think a driverless car wouldn’t have an accident, but who is liable? Were autonomous vehicles still developed, or should we limit them to semi-autonomous ones that aid people in driving? A jury is still out on this, but these ethical debates occur as AI advances.
Job impact of AI:
Public perception of artificial intelligence often centers around job losses, but this should refrain. The market demand for specific job roles shifts with every disruptive technology. Automobile manufacturers like GM are turning to electric vehicle production to align with green initiatives. Energy isn’t going away, but the energy source is shifting from a fuel economy to an electric economy.
The demand for jobs will also shift as a result of artificial intelligence. Management of AI systems will require individuals. Jobs most likely affected by job demand shifts, such as customer service, will still need people to solve more complex problems. With artificial intelligence and its effect on the job market, the biggest challenge will be helping people transition to new roles.
Usually, privacy is discussed in terms of data protection, security, and privacy. In recent years, policymakers have made more progress due to these concerns. States are developing policies like the California Consumer Privacy Act (CCPA), which requires businesses to inform consumers about data collection. Personal information (PII) has to be stored and used differently in response to legislation like this. Security investments have become a priority for businesses as they seek to eliminate vulnerabilities and opportunities for surveillance, hacking, and cyberattacks.
Discrimination and bias
Several machine learning systems are biased and discriminatory. Is safeguarding against bias and discrimination possible when the training data is subjective? It’s common for companies to have good intentions for their automation efforts, but Reuters (link resides outside IBM) highlights some unforeseen consequences. Amazon unintentionally discriminated against job candidates for technical roles by gender in their attempt to automate and simplify a process. The Harvard Business Review (link outside IBM) has raised other questions about AI in hiring, such as what data you can use when evaluating a candidate. Read More
Businesses have also become more active in this discussion around AI ethics and values as they become aware of the risks of AI. IBM has retired its general-purpose facial recognition and analysis products. CEO Arvind Krishna wrote: “IBM strongly opposes and will not condone the use of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of human rights, or any purpose in conflict with our principles and values.”
An accountability system:
There is no significant legislation to regulate AI practices, so there is no enforcement mechanism to ensure ethical AI. Companies are currently motivated to be honorable by the negative consequences of unethical AI. As a result of a collaboration between ethicists and researchers, ethical frameworks have emerged to govern AI model construction and distribution. At the moment, they only serve as guides. In some studies, distributed responsibility and a lack of foresight into potential consequences do not contribute to preventing harm to society (PDF, 1 MB).