Artificial intelligence (AI) is transforming the healthcare industry in unprecedented ways. From diagnosis to treatment, from research to management, AI is enabling new possibilities and improving outcomes. Here are five things to expect from AI in healthcare in 2024:
AI will augment human doctors, not replace them. AI can assist doctors with tasks such as analyzing medical images, generating reports, recommending treatments, and monitoring patients. However, AI cannot replace the human touch, empathy, and ethical judgment that doctors provide. AI will enhance the capabilities of doctors, not threaten their jobs.
AI will personalize medicine and improve patient experience. AI can help tailor treatments and interventions to each patient’s specific needs and preferences. For example, AI can use genomic data to identify the best drugs for a patient or use behavioral data to nudge a patient to adhere to a treatment plan. AI can also improve patient experience by providing chatbots, virtual assistants, and telemedicine services.
AI will accelerate drug discovery and development. AI can help discover new drugs and test their efficacy and safety faster and cheaper than traditional methods. For example, AI can use natural language processing to mine scientific literature, use computer vision to screen compounds, use machine learning to predict drug interactions use deep learning to design new molecules.
AI will improve healthcare operations and efficiency. AI can help optimize healthcare processes and resources, such as scheduling appointments, managing inventory, allocating staff, and reducing waste. For example, AI can use predictive analytics to forecast demand, use reinforcement learning to optimize workflows, use computer vision to monitor equipment use natural language processing to automate documentation.
AI will democratize healthcare access and quality. AI can help overcome the barriers of cost, distance, and availability that prevent many people from accessing quality healthcare. For example, AI can provide low-cost diagnostic tools, remote consultation services, and online education platforms that can reach underserved populations and regions.
Nvidia, LG, Sony, and Samsung kicked off the event with significant announcements. While the livestreams have concluded, the show floor remains open for more exciting reveals.
Notable highlights include Coast Runner, a new entrant in the desktop CNC milling industry, aiming to make CNC technology accessible to everyone through power, compact size, and education.
Smart pepper spray 444 is back, now with a Mace partnership and plans for manufacturing.
Helpful AI products showcased at CES include voice synthesis and safer phones for kids.
Hydrogen-powered vehicles made an appearance, sparking discussions about their resurgence.
The term “AI” has been used in computer science since the 1950s, but most people outside the industry didn’t start talking about it until the end of 2022. That’s because recent advances in machine learning led to big breakthroughs that are beginning to have a profound impact on nearly every aspect of our lives. We’re here to help break down some of the buzzwords so you can better understand AI terms and be part of the global conversation.
Artificial intelligence Artificial intelligence is basically a super-smart computer system that can imitate humans in some ways, like comprehending what people say, making decisions, translating between languages, analyzing if something is negative or positive, and even learning from experience. It’s artificial in that its intellect was created by humans using technology. Sometimes people say AI systems have digital brains, but they’re not physical machines or robots — they’re programs that run on computers. They work by putting a vast collection of data through algorithms, which are sets of instructions, to create models that can automate tasks that typically require human intelligence and time. Sometimes people specifically engage with an AI system — like asking Bing Chat for help with something — but more often the AI is happening in the background all around us, suggesting words as we type, recommending songs in playlists and providing more relevant information based on our preferences.
Machine learning If artificial intelligence is the goal, machine learning is how we get there. It’s a field of computer science, under the umbrella of AI, where people teach a computer system how to do something by training it to identify patterns and make predictions based on them. Data is run through algorithms over and over, with different input and feedback each time to help the system learn and improve during the training process — like practicing piano scales 10 million times in order to sight-read music going forward. It’s especially helpful with problems that would otherwise be difficult or impossible to solve using traditional programming techniques, such as recognizing images and translating languages. It takes a huge amount of data, and that’s something we’ve only been able to harness in recent years as more information has been digitized and as computer hardware has become faster, smaller, more powerful and better able to process all that information. That’s why large language models that use machine learning — such as Bing Chat and ChatGPT — have suddenly arrived on the scene.
Large language models Large language models, or LLMs, use machine learning techniques to help them process language so they can mimic the way humans communicate. They’re based on neural networks, or NNs, which are computing systems inspired by the human brain — sort of like a bunch of nodes and connections that simulate neurons and synapses. They are trained on a massive amount of text to learn patterns and relationships in language that help them use human words. Their problem-solving capabilities can be used to translate languages, answer questions in the form of a chatbot, summarize text and even write stories, poems and computer code. They don’t have thoughts or feelings, but sometimes they sound like they do, because they’ve learned patterns that help them respond the way a human might. They’re
often fine-tuned by developers using a process called reinforcement learning from human feedback (RLHF) to help them sound more conversational.
Generative AI Generative AI leverages the power of large language models to make new things, not just regurgitate or provide information about existing things. It learns patterns and structures and then generates something that’s similar but new. It can make things like pictures, music, text, videos and code. It can be used to create art, write stories, design products and even help doctors with administrative tasks. But it can also be used by bad actors to create fake news or pictures that look like photographs but aren’t real, so tech companies are working on ways to clearly identify AI-generated content.
Hallucinations Generative AI systems can create stories, poems and songs, but sometimes we want results to be based in truth. Since these systems can’t tell the difference between what’s real and fake, they can give inaccurate responses that developers refer to as hallucinations or confabulations — much like if someone saw what looked like the outlines of a face on the moon and began saying there was an actual man in the moon. Developers try to resolve these issues through “grounding,” which is when they provide an AI system with additional information from a trusted source to improve accuracy about a specific topic. Sometimes a system’s predictions are wrong, too, if a model doesn’t have current l doesn’t have current information after it’s trained.
Responsible AI Responsible AI guides people as they try to design systems that are safe and fair — at every level, including the machine learning model, the software, the user interface and the rules and restrictions put in place to access an application. It’s a crucial element because these systems are often tasked with helping make important decisions about people, such as in education and healthcare, but since they’re created by humans and trained on data from an imperfect world, they can reflect any inherent biases. A big part of responsible AI involves understanding the data that was used to train the systems and finding ways to mitigate any shortcomings to help better reflect society at large, not just certain groups of people.
Multimodal models A multimodal model can work with different types, or modes, of data simultaneously. It can look at pictures, listen to sounds and read words. It’s the ultimate multitasker! It can combine all of this information to do things like answer questions about images.
Prompts A prompt is an instruction entered into a system in language, images or code that tells the AI what task to perform. Engineers — and really all of us who interact with AI systems — must carefully design prompts to get the desired outcome from the large language models. It’s like placing your order at a deli counter: You don’t just ask for a sandwich, but you specify which bread you want and the type and amounts of condiments, vegetables, cheese and meat to get a lunch that you’ll find delicious and nutritious.
Copilots A copilot is like a personal assistant that works alongside you in all sorts of digital applications, helping with things like writing, coding, summarizing and searching. It can also help you make decisions and understand lots of data. The recent development of large language models made copilots possible, allowing them to comprehend natural human language and provide answers, create content or take action as you work within different computer programs. Copilots are built with Responsible AI guardrails to make sure they’re safe and secure and are used in a good way. Just like a copilot in an airplane, it’s not in charge — you are — but it’s a tool that can help you be more productive and efficient.
Plugins Plugins are like relief pitchers in baseball — they step in to fill specific needs that might pop up as the game develops, such as putting in a left-handed pitcher when a left-handed hitter steps up to the plate for a crucial at-bat. Plugins enable AI applications to do more things without having to modify the underlying model. They are what allow copilots to interact with other software and services, for example. They can help AI systems access new information, do complicated math or talk to other programs. They make AI systems more powerful by connecting them to the rest of the digital world.
As technology continues to rapidly develop, the idea of artificial intelligence (AI) taking over human roles and being present in everyday life is becoming a reality. Today, AI programs such as Midjourney and DALL-E are capable of creating hyper-realistic images and complex works of art in a matter of seconds. Meanwhile, Open AI’s ChatGPT started making headlines in March 2023 for its impressive ability to craft well-written essays, solve mathematical problems, code, and engage in conversations with users. As tech magnate Bill Gates said, humanity has entered the AI age.
Numerous industries—from not-for-profit organizations to e-commerce and gaming—already are looking for ways to integrate AI into their operations. While some fear that AI will replace human labor and creativity, others are tapping into the technology’s potential to improve how businesses interact with their customers.
In what follows, we break down three remarkable ways in which artificial intelligence is facilitating customer loyalty.
Data collection
One of the main strengths of AI is its ability to collect and organize high volumes of user-generated data. This type of data is incredibly valuable for any business, as it reveals a lot about the customer, including website interactions, purchase history, and social media engagement. In isolation, that data may not mean much. However, once analyzed by AI, it can be streamlined and converted into digestible insights and visual charts.
Taking that data into consideration, marketers can make informed decisions that drive marketing strategies, targeted and market-entry campaigns, product launches, and more.
Personalized recommendations and offers
By using collected data and specialized algorithms, AI can assist in delivering customized experiences and interest-based content to individual customers, which makes them feel as if the brand understands their preferences and tastes.
Perhaps one of the most notorious examples is content recommendations on streaming platforms. On its Help Center site, Netflix clearly states that they “estimate the likelihood that you will watch a particular title in our catalog based on a number of factors”. Such factors include the user’s viewing history and rating of previous titles coupled with the information provided by other members with similar tastes and information about the titles (genre, actors, release year, etc).
Another industry that utilizes this strategy is gaming, particularly casino platforms. Overall, reputable casino sites focus on building customer loyalty by offering a number of benefits to players, including deposit bonuses, free spins, and VIP programs. But, with AI, they can go a step further and tailor their messaging and content to each individual player.
24/7 support availability and quick responses
Customer service is a detrimental factor in customer loyalty. In fact, 83% of customers agree that good customer support will turn them from one-time shoppers into lifers.
With the help of chatbot technology, businesses can provide their customers with automated customer service that is available around the clock. Medium to large-scale businesses and brands likely have customers all over the world. This means that human customer support agents won’t be always able to address urgent requests and issues from customers residing in different time zones. In this way, AI-powered chatbots are a convenient solution.
Using AI assistants is also a great way to ensure that customers get quick responses to simple queries whenever customer support agents are too busy solving more complex cases.
To conclude
Artificial intelligence has the potential to change the way businesses interact with their customers, giving them a more personalized and meaningful experience. This helps to strengthen customer satisfaction and loyalty, ultimately increasing the business’ overall success.
Just as the industrial revolution changed how people worked, the AI revolution is altering how people communicate with businesses.
Microsoft has announced a suite of new AI solutions and improvements to Microsoft Cloud for Nonprofit. These solutions are designed to transform the nonprofit industry by helping fundraisers engage with donors, manage campaigns, and optimize operations. The company also announced a limited private preview for nonprofits to experience a new AI-powered fundraising propensity model. This model will allow participating nonprofits to test new AI tools that can predict fundraising goals and identify donors most likely to donate to a campaign or cause.
According to Justin Spelhaug, Vice President and Global Head of Tech for Social Impact at Microsoft, “AI can and will be a game-changer for nonprofits and fundraisers.” With these new AI solutions, Microsoft aims to empower nonprofits and fundraisers to achieve more and create a more positive and rewarding experience for donors.
Nonprofits often struggle to make budgets go further and do more with fewer resources. Since 2020, many fundraisers have seen a decline in the number of donors giving to causes. Data can help nonprofits identify promising prospects, predict donor behavior, and measure fundraising outcomes. However, access to data and the ability to form actionable insights based on fundraising analytics is a major roadblock for many organizations.
Microsoft Cloud for Nonprofit new AI solutions
Microsoft Cloud for Nonprofit now includes new tools such as a Fundraising dashboard built on Power BI’s data visualization platform, Dynamics 365 Marketing integration, and AI capabilities in the Fundraising and Engagement tools. These tools will help nonprofits leverage data in a powerful way to attract, retain, and grow their donor base.
In addition to these new capabilities in Microsoft Cloud for Nonprofit, Microsoft is developing an AI-based donor propensity model. This model will help nonprofits identify constituents most likely to donate or engage with a campaign or cause based on predictive donor behavior. Interested nonprofits can sign up to participate in this private preview.
These new AI solutions are built on Microsoft’s trusted cloud platform, Azure, which offers enhanced security, scalability, and reliability. They are also integrated with Microsoft’s existing solutions for nonprofits such as Microsoft 365, Power Platform, Microsoft Dynamics 365, and LinkedIn.