Världens tredje första Halland Tech Meet gick av stapeln den 8 april 2021 under ledning av HighFive. Precis som efter tidigare tillfällen skapades inspirerande samtal där många nya lärdomar togs med från samtliga programpass. Vad innebär egentligen begreppet gamification/spelifiering och hur kan vi använda det för att effektivisera vår vardag? Vilka utmaningar kan vi hantera i våra bolag och samhället i stort med hjälp av AI och hur kommer matproduktionen att förändras om vi tar in kunskapen från agtech och foodtech? Svaret på dessa frågor och många fler kan du ta del av i klippen nedan.

Den 26 november 2020 anordnades Halland Tech Meet för andra gången under året. Förra gången pratade vi mycket om människocentrerad AI, denna gång gjorde vi ett djupdyk i vår uppkopplade värld, smarta städer samt hur teknik och AI kan agera som en möjliggörande kraft för en bättre och mer individanpassad hälsa. I samtalen medverkade ett gäng intressanta personer som inspirerade oss alla på ett eller annat sätt. Missade du att se det live? Ta del av samtalen nedan!

On May 13, 2020, Halland Tech Meet was organized, a digital event that brings together business leaders and organizations in Halland that have found new ways to act in the current situation. More than 200 people reported their participation to the digital event. In the sessions, r360's Chairman Ludvig Granberg, Lisa Gramnäs from Gramtec Development, participated, Pontus Wärnestål, senior lecturer at the Academy of Information Technology at Halmstad University and Malin Kjällström from Connect West / Gothenburg Tech Week, among others.

Här finner ni även samtalet i poddformat

What kind of job is being replaced by robots?

Hard to say what exact roles are needed in an organization going forward, when it comes to expertise, it may be easier to regard following:

• Someone who knows the basics of data analysis / machine learning. For example, you can hire or train your existing staff using online courses.
• Someone who knows about the company's processes and can decide / prioritize which processes can benefit from data-driven solutions, as well as what improvements would "pay off".
• Someone who understands your customers and services and can help design data collection methods as well as data-driven services according to their needs, such as a UX designer.
• All of these skills must work together as interactively as possible.

My suggestion is that once you have a goal for what you want to achieve in the organization, it is easier to determine what expertise is needed. Then keep in mind that a master student in data mining could produce certain models as part of his dissertation work if there is a clear goal and data.

Hur är sk generativ design där människan sätter ett framework och där AI ger en mängd förslag på lösningar inom "frameworket". Specialister behövs för att välja och utvärdera men bulken görs av AI.

What ethical dilemmas do you see with human-centered AI?

• How to handle sensitive information. AI requires data, in a digital format, to be stored and transferred. The data is often stored on the cloud for ease of access, and creates vulnerability, especially when the data is sensitive and valuable. Humans might not be the best at keeping secrets either. Just like we have procedures in place to limit how people share information, we have to put procedures in place that limit how machines share information.
  
  
• What to do about predictions. Consider the movie “Minority Report” where the police started arresting people that were “going to” commit a crime because of a prediction by an oracle. AI prediction systems are in effect an oracle that tries to determine what will likely happen with a certain level of confidence. When the events they are trying to predict are of a sensitive nature then the effects of either being wrong or being right must be considered and contingency plans must be put in place.
  
  
• Bias and inclusion. Bias is a source of error. Humans are extremely biased in their decision making process as well. If you are interested in this topic I recommend the book “Thinking, Fast and Slow” by Daniel Kahneman. Machines are also biased by the data that they are trained on. If there are not enough examples covering the entire distribution of inputs, say not enough women represented in a sample, then the AI will not be good at modeling those missing elements. No model is without bias, but by being aware of the model’s limitations, we can work towards an acceptable compromise.
  
  
• How to handle errors. Both humans and machines can be wrong in their decisions. In the case of a medical error, for example, there is insurance. If a doctor makes the wrong diagnostic he may be sued for malpractice. What happens if an AI agent makes the wrong diagnostic? You may also consider that blood tests and other medical tests are not 100% accurate and there is always a risk. The way we deal with this situation today is we assign someone to be ultimately responsible for the decision, the doctor, the lab technician, or the person supervising the AI, for example. However, the law needs to be extended to include certain use-cases where the “supervisor” is not clearly defined.